{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:57:40Z","timestamp":1780473460710,"version":"3.54.1"},"reference-count":316,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10956-y","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T11:20:51Z","timestamp":1736508051000},"page":"2239-2284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Federated and transfer learning for cancer detection based on image analysis"],"prefix":"10.1007","volume":"37","author":[{"given":"Amine","family":"Bechar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafik","family":"Medjoudj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youssef","family":"Elmir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8904-5587","authenticated-orcid":false,"given":"Yassine","family":"Himeur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abbes","family":"Amira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"10956_CR1","doi-asserted-by":"crossref","unstructured":"Anukriti A, Dhasmana S, Uniyal P, Somvanshi U, Bhardwaj M, Gupta S, Haque M, Lohani D, Kumar J. Ruokolainen et al (2019) Investigation of precise molecular mechanistic action of tobacco-associated carcinogen \u2018nnk\u2019 induced carcinogenesis: A system biology approach. Genes 10(8):564","DOI":"10.3390\/genes10080564"},{"key":"10956_CR2","doi-asserted-by":"crossref","unstructured":"Shrivastava D, Sanyal S, Maji AK, Kandar D (2020) Bone cancer detection using machine learning techniques. In: Smart Healthcare for Disease Diagnosis and Prevention, Elsevier, pp 175\u2013183","DOI":"10.1016\/B978-0-12-817913-0.00017-1"},{"key":"10956_CR3","doi-asserted-by":"crossref","unstructured":"Hamza A, Lekouaghet B, Himeur Y (2023) Hybrid whale-mud-ring optimization for precise color skin cancer image segmentation. In: 2023 6th international conference on signal processing and information security (ICSPIS), IEEE, pp 87\u201392","DOI":"10.1109\/ICSPIS60075.2023.10343708"},{"issue":"3\u20132","key":"10956_CR4","first-page":"21","volume":"10","author":"M Tahmooresi","year":"2018","unstructured":"Tahmooresi M, Afshar A, Rad BB, Nowshath K, Bamiah M (2018) Early detection of breast cancer using machine learning techniques. J Telecommun Comput Eng (JTEC) 10(3\u20132):21\u201327","journal-title":"J Telecommun Comput Eng (JTEC)"},{"key":"10956_CR5","doi-asserted-by":"crossref","unstructured":"Bechar A, Elmir Y, Medjoudj R, Himeur Y, Amira A (2023) Harnessing transformers: A leap forward in lung cancer image detection. In: 2023 6th international conference on signal processing and information security (ICSPIS), IEEE, pp 218\u2013223","DOI":"10.1109\/ICSPIS60075.2023.10344192"},{"issue":"4","key":"10956_CR6","first-page":"591","volume":"7","author":"K Pradhan","year":"2020","unstructured":"Pradhan K, Chawla P (2020) Medical internet of things using machine learning algorithms for lung cancer detection. J Manag Anal 7(4):591\u2013623","journal-title":"J Manag Anal"},{"key":"10956_CR7","doi-asserted-by":"crossref","unstructured":"Farrelly C, Singh Y, Hathaway QA, Carlsson G, Choudhary A, Paul R, Doretto G, Himeur Y, Atalls S, Mansoor W (2023) Current topological and machine learning applications for bias detection in text. In: 2023 6th international conference on signal processing and information security (ICSPIS), IEEE, pp 190\u2013195","DOI":"10.1109\/ICSPIS60075.2023.10343824"},{"key":"10956_CR8","doi-asserted-by":"crossref","unstructured":"Wu Q, Zhao W (2017) Small-cell lung cancer detection using a supervised machine learning algorithm. In: 2017 international symposium on computer science and intelligent controls (ISCSIC), IEEE, pp 88\u201391","DOI":"10.1109\/ISCSIC.2017.22"},{"issue":"2","key":"10956_CR9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/technologies11020040","volume":"11","author":"M Iman","year":"2023","unstructured":"Iman M, Arabnia HR, Rasheed K (2023) A review of deep transfer learning and recent advancements. Technologies 11(2):40","journal-title":"Technologies"},{"key":"10956_CR10","doi-asserted-by":"crossref","unstructured":"Wittkopp T, Acker A (2021) Decentralized federated learning preserves model and data privacy. In: service-oriented computing\u2013ICSOC 2020 workshops: AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Dubai, United Arab Emirates, December 14\u201317, 2020, Proceedings, Springer, pp 176\u2013187","DOI":"10.1007\/978-3-030-76352-7_20"},{"key":"10956_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105698","volume":"119","author":"Y Himeur","year":"2023","unstructured":"Himeur Y, Al-Maadeed S, Kheddar H, Al-Maadeed N, Abualsaud K, Mohamed A, Khattab T (2023) Video surveillance using deep transfer learning and deep domain adaptation: towards better generalization. Eng Appl Artif Intell 119:105698","journal-title":"Eng Appl Artif Intell"},{"key":"10956_CR12","doi-asserted-by":"crossref","unstructured":"Kheddar H, Himeur Y, Al-Maadeed S, Amira A, Bensaali F (2023) Deep transfer learning for automatic speech recognition: Towards better generalization, arXiv preprint arXiv:2304.14535","DOI":"10.1016\/j.knosys.2023.110851"},{"key":"10956_CR13","unstructured":"Bousbiat H, Bousselidj R, Himeur Y, Amira A, Bensaali F, Fadli F, Mansoor W, Elmenreich W (2023) Crossing roads of federated learning and smart grids: Overview, challenges, and perspectives, arXiv preprint arXiv:2304.08602"},{"key":"10956_CR14","doi-asserted-by":"crossref","unstructured":"Razavi-Far R, Wang B, Taylor ME, Yang Q (2022) An introduction to federated and transfer learning. In: Federated and Transfer Learning, Springer, pp 1\u20136","DOI":"10.1007\/978-3-031-11748-0_1"},{"issue":"4","key":"10956_CR15","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1002\/ijc.33588","volume":"149","author":"J Ferlay","year":"2021","unstructured":"Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Pi\u00f1eros M, Znaor A, Bray F (2021) Cancer statistics for the year 2020: an overview. Int J cancer 149(4):778\u2013789","journal-title":"Int J cancer"},{"issue":"1","key":"10956_CR16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1102\/1470-7330.2005.0018","volume":"5","author":"RA Castellino","year":"2005","unstructured":"Castellino RA (2005) Computer aided detection (cad): an overview. Cancer Imag 5(1):17","journal-title":"Cancer Imag"},{"issue":"1","key":"10956_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1177\/0284185118770917","volume":"60","author":"EL Henriksen","year":"2019","unstructured":"Henriksen EL, Carlsen JF, Vejborg IM, Nielsen MB, Lauridsen CA (2019) The efficacy of using computer-aided detection (cad) for detection of breast cancer in mammography screening: a systematic review. Acta radiol 60(1):13\u201318","journal-title":"Acta radiol"},{"issue":"1","key":"10956_CR18","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1148\/radiol.2015141959","volume":"277","author":"L Morra","year":"2015","unstructured":"Morra L, Sacchetto D, Durando M, Agliozzo S, Carbonaro LA, Delsanto S, Pesce B, Persano D, Mariscotti G, Marra V et al (2015) Breast cancer: computer-aided detection with digital breast tomosynthesis. Radiology 277(1):56\u201363","journal-title":"Radiology"},{"key":"10956_CR19","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","volume":"145","author":"PR Jeyaraj","year":"2019","unstructured":"Jeyaraj PR, Samuel Nadar ER (2019) Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J Cancer Res Clin Oncol 145:829\u2013837","journal-title":"J Cancer Res Clin Oncol"},{"issue":"14","key":"10956_CR20","doi-asserted-by":"publisher","first-page":"3608","DOI":"10.3390\/cancers15143608","volume":"15","author":"X Jiang","year":"2023","unstructured":"Jiang X, Hu Z, Wang S, Zhang Y (2023) Deep learning for medical image-based cancer diagnosis. Cancers 15(14):3608","journal-title":"Cancers"},{"issue":"5","key":"10956_CR21","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/MS.2016.114","volume":"33","author":"C Ebert","year":"2016","unstructured":"Ebert C, Louridas P (2016) Machine learning. IEEE Softw 33(5):110\u2013115","journal-title":"IEEE Softw"},{"issue":"1","key":"10956_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranon.2020.100907","volume":"14","author":"Y Xie","year":"2021","unstructured":"Xie Y, Meng W-Y, Li R-Z, Wang Y-W, Qian X, Chan C, Yu Z-F, Fan X-X, Pan H-D, Xie C et al (2021) Early lung cancer diagnostic biomarker discovery by machine learning methods. Trans Oncol 14(1):100907","journal-title":"Trans Oncol"},{"issue":"4","key":"10956_CR23","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.icte.2020.04.009","volume":"6","author":"AR Vaka","year":"2020","unstructured":"Vaka AR, Soni B, Reddy S (2020) Breast cancer detection by leveraging machine learning. Ict Express 6(4):320\u2013324","journal-title":"Ict Express"},{"issue":"3","key":"10956_CR24","first-page":"2032","volume":"34","author":"MZ Awan","year":"2024","unstructured":"Awan MZ, Arif MS, Abideen MZU, Abodayeh K (2024) Comparative analysis of machine learning models for breast cancer prediction and diagnosis: A dual-dataset approach. Indones J Electr Eng Comput Sci 34(3):2032\u20132044","journal-title":"Indones J Electr Eng Comput Sci"},{"issue":"5","key":"10956_CR25","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1093\/bioinformatics\/btz769","volume":"36","author":"R Chen","year":"2020","unstructured":"Chen R, Yang L, Goodison S, Sun Y (2020) Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics 36(5):1476\u20131483","journal-title":"Bioinformatics"},{"key":"10956_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12859-017-1798-2","volume":"18","author":"JD Young","year":"2017","unstructured":"Young JD, Cai C, Lu X (2017) Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma. BMC bioinform 18:5\u201317","journal-title":"BMC bioinform"},{"key":"10956_CR27","doi-asserted-by":"crossref","unstructured":"Mohammed SA, Darrab S, Noaman SA, Saake G (2020) Analysis of breast cancer detection using different machine learning techniques. In: Data Mining and Big Data: 5th international conference, DMBD 2020, Belgrade, Serbia, July 14\u201320, 2020, Proceedings 5, Springer, pp 108\u2013117","DOI":"10.1007\/978-981-15-7205-0_10"},{"key":"10956_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","volume":"460","author":"A Mehmood","year":"2021","unstructured":"Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Maqsood M, Yaqub M (2021) A transfer learning approach for early diagnosis of alzheimer\u2019s disease on mri images. Neuroscience 460:43\u201352","journal-title":"Neuroscience"},{"key":"10956_CR29","doi-asserted-by":"crossref","unstructured":"Zheng Y, Li C, Zhou X, Chen H, Xu H, Li Y, Zhang H, Li X, Sun H, Huang X, et al (2022) Application of transfer learning and ensemble learning in image-level classification for breast histopathology. Intell Med","DOI":"10.1016\/j.imed.2022.05.004"},{"issue":"9","key":"10956_CR30","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3390\/fi14090246","volume":"14","author":"T Alam","year":"2022","unstructured":"Alam T, Gupta R (2022) Federated learning and its role in the privacy preservation of iot devices. Future Int 14(9):246","journal-title":"Future Int"},{"issue":"1","key":"10956_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):1\u201340","journal-title":"J Big Data"},{"key":"10956_CR32","doi-asserted-by":"crossref","unstructured":"Meghana K, Nandal N, Tanwar R, Goel L, Chhabra G (2023) Breast cancer detection with machine learning-a review. In: 2023 international conference on sustainable computing and data communication systems (ICSCDS), IEEE, pp 168\u2013172","DOI":"10.1109\/ICSCDS56580.2023.10104644"},{"key":"10956_CR33","doi-asserted-by":"crossref","unstructured":"Rani R, Sahoo J, Bellamkonda S (2023) Application of deep transfer learning in detection of lung cancer: A systematic survey. In: 2022 OPJU international technology conference on emerging technologies for sustainable development (OTCON), IEEE, pp 1\u20136","DOI":"10.1109\/OTCON56053.2023.10113932"},{"key":"10956_CR34","doi-asserted-by":"crossref","unstructured":"Coelho KK, Nogueira M, Vieira AB, Silva EF, Nacif JA (2023) A survey on federated learning for security and privacy in healthcare applications. Comput Commun","DOI":"10.1016\/j.comcom.2023.05.012"},{"issue":"4","key":"10956_CR35","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1007\/s10586-022-03658-4","volume":"26","author":"A Rahman","year":"2023","unstructured":"Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS (2023) Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Clust Comput 26(4):2271\u20132311","journal-title":"Clust Comput"},{"key":"10956_CR36","doi-asserted-by":"crossref","unstructured":"Chowdhury A, Kassem H, Padoy N, Umeton R, Karargyris A (2021) A review of medical federated learning: Applications in oncology and cancer research. In: international MICCAI Brainlesion Workshop, Springer, pp 3\u201324","DOI":"10.1007\/978-3-031-08999-2_1"},{"issue":"4","key":"10956_CR37","doi-asserted-by":"publisher","first-page":"738","DOI":"10.3390\/cancers13040738","volume":"13","author":"G Ayana","year":"2021","unstructured":"Ayana G, Dese K, Choe S-W (2021) Transfer learning in breast cancer diagnoses via ultrasound imaging. Cancers 13(4):738","journal-title":"Cancers"},{"issue":"5","key":"10956_CR38","doi-asserted-by":"publisher","first-page":"7374","DOI":"10.1109\/JIOT.2023.3329061","volume":"11","author":"A Rauniyar","year":"2024","unstructured":"Rauniyar A, Hagos DH, Jha D, H\u00e5keg\u00e5rd JE, Bagci U, Rawat DB, Vlassov V (2024) Federated learning for medical applications: a taxonomy, current trends, challenges, and future research directions. IEEE Int Things J 11(5):7374\u20137398. https:\/\/doi.org\/10.1109\/JIOT.2023.3329061","journal-title":"IEEE Int Things J"},{"key":"10956_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100819","volume":"28","author":"MK Hasan","year":"2022","unstructured":"Hasan MK, Elahi MTE, Alam MA, Jawad MT, Mart\u00ed R (2022) Dermoexpert: skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation. Inf Med Unlocked 28:100819","journal-title":"Inf Med Unlocked"},{"key":"10956_CR40","first-page":"141","volume":"I","author":"Y Kumar","year":"2021","unstructured":"Kumar Y, Singla R (2021) Federated learning systems for healthcare: perspective and recent progress. Fed Learn Syst Tow Next Gener A I:141\u2013156","journal-title":"Fed Learn Syst Tow Next Gener A"},{"issue":"4","key":"10956_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533708","volume":"3","author":"M Joshi","year":"2022","unstructured":"Joshi M, Pal A, Sankarasubbu M (2022) Federated learning for healthcare domain-pipeline, applications and challenges. ACM Trans Comput Healthc 3(4):1\u201336","journal-title":"ACM Trans Comput Healthc"},{"key":"10956_CR42","unstructured":"Kone\u010dn\u1ef3 J, McMahan HB, Yu FX, Richt\u00e1rik P, Suresh AT, Bacon D (2016) Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492"},{"issue":"1","key":"10956_CR43","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1443","volume":"12","author":"B Yu","year":"2022","unstructured":"Yu B, Mao W, Lv Y, Zhang C, Xie Y (2022) A survey on federated learning in data mining, Wiley interdisciplinary reviews. Data Min Knowl Discov 12(1):e1443","journal-title":"Data Min Knowl Discov"},{"issue":"8","key":"10956_CR44","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1109\/TPDS.2020.2975189","volume":"31","author":"W Liu","year":"2020","unstructured":"Liu W, Chen L, Chen Y, Zhang W (2020) Accelerating federated learning via momentum gradient descent. IEEE Trans Parallel Distrib Syst 31(8):1754\u20131766","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"10956_CR45","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.ins.2022.11.031","volume":"619","author":"X Li","year":"2023","unstructured":"Li X, Zhao S, Chen C, Zheng Z (2023) Heterogeneity-aware fair federated learning. Inf Sci 619:968\u2013986","journal-title":"Inf Sci"},{"key":"10956_CR46","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TSIPN.2022.3151242","volume":"8","author":"W Liu","year":"2022","unstructured":"Liu W, Chen L, Zhang W (2022) Decentralized federated learning: balancing communication and computing costs. IEEE Trans Signal Inf Process Over Netw 8:131\u2013143","journal-title":"IEEE Trans Signal Inf Process Over Netw"},{"key":"10956_CR47","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.ins.2021.12.102","volume":"589","author":"W Huang","year":"2022","unstructured":"Huang W, Li T, Wang D, Du S, Zhang J, Huang T (2022) Fairness and accuracy in horizontal federated learning. Inf Sci 589:170\u2013185","journal-title":"Inf Sci"},{"issue":"6","key":"10956_CR48","doi-asserted-by":"publisher","DOI":"10.2196\/26598","volume":"9","author":"D Cha","year":"2021","unstructured":"Cha D, Sung M, Park Y-R et al (2021) Implementing vertical federated learning using autoencoders: Practical application, generalizability, and utility study. JMIR Med Inf 9(6):e26598","journal-title":"JMIR Med Inf"},{"key":"10956_CR49","doi-asserted-by":"crossref","unstructured":"Zhang R, Li H, Hao M, Chen H, Zhang Y (2022) Secure feature selection for vertical federated learning in ehealth systems. In: ICC 2022-IEEE international conference on communications, IEEE, pp 1257\u20131262","DOI":"10.1109\/ICC45855.2022.9838917"},{"key":"10956_CR50","doi-asserted-by":"crossref","unstructured":"Yu C, Shen S, Wang S, Zhang K, Zhao H (2024) Communication-efficient hybrid federated learning for e-health with horizontal and vertical data partitioning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2024.3383748"},{"issue":"5","key":"10956_CR51","doi-asserted-by":"publisher","first-page":"bbad269","DOI":"10.1093\/bib\/bbad269","volume":"24","author":"Q Wang","year":"2023","unstructured":"Wang Q, He M, Guo L, Chai H (2023) Afei: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration. Brief Bioinf 24(5):bbad269","journal-title":"Brief Bioinf"},{"issue":"5","key":"10956_CR52","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/TAI.2021.3139055","volume":"4","author":"H Zhu","year":"2021","unstructured":"Zhu H, Wang R, Jin Y, Liang K (2021) Pivodl: Privacy-preserving vertical federated learning over distributed labels. IEEE Trans Artif Intell 4(5):988\u20131001","journal-title":"IEEE Trans Artif Intell"},{"key":"10956_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104658","volume":"158","author":"T-T Kuo","year":"2022","unstructured":"Kuo T-T, Pham A (2022) Detecting model misconducts in decentralized healthcare federated learning. Int J Med Inf 158:104658","journal-title":"Int J Med Inf"},{"issue":"3","key":"10956_CR54","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/JSTSP.2022.3152445","volume":"16","author":"H Ye","year":"2022","unstructured":"Ye H, Liang L, Li GY (2022) Decentralized federated learning with unreliable communications. IEEE J Sel Top Signal process 16(3):487\u2013500","journal-title":"IEEE J Sel Top Signal process"},{"key":"10956_CR55","unstructured":"Beltr\u00e1n ETM, P\u00e9rez MQ, S\u00e1nchez PMS, Bernal SL, Bovet G, P\u00e9rez MG, P\u00e9rez GM, Celdr\u00e1n AH (2023) Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges. IEEE Communications Surveys & Tutorials"},{"key":"10956_CR56","doi-asserted-by":"crossref","unstructured":"Yang A, Ma Z, Zhang C, Han Y, Hu Z, Zhang W, Huang X, Wu Y (2022) Review on application progress of federated learning model and security hazard protection. Digit Commun Netw","DOI":"10.1016\/j.dcan.2022.11.006"},{"issue":"8","key":"10956_CR57","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.jacr.2022.03.016","volume":"19","author":"E Darzidehkalani","year":"2022","unstructured":"Darzidehkalani E, Ghasemi-Rad M, van Ooijen P (2022) Federated learning in medical imaging: Part ii: methods, challenges, and considerations. J Am College Radiol 19(8):975\u2013982","journal-title":"J Am College Radiol"},{"key":"10956_CR58","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.neucom.2022.11.011","volume":"518","author":"JS-P D\u00edaz","year":"2023","unstructured":"D\u00edaz JS-P, Garc\u00eda \u00c1L (2023) Study of the performance and scalability of federated learning for medical imaging with intermittent clients. Neurocomputing 518:142\u2013154","journal-title":"Neurocomputing"},{"issue":"1","key":"10956_CR59","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proceed IEEE 109(1):43\u201376","journal-title":"Proceed IEEE"},{"key":"10956_CR60","doi-asserted-by":"publisher","first-page":"196197","DOI":"10.1109\/ACCESS.2020.3034343","volume":"8","author":"G Vrban\u010di\u010d","year":"2020","unstructured":"Vrban\u010di\u010d G, Podgorelec V (2020) Transfer learning with adaptive fine-tuning. IEEE Access 8:196197\u2013196211","journal-title":"IEEE Access"},{"key":"10956_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105874","volume":"199","author":"M De Bois","year":"2021","unstructured":"De Bois M, El Yacoubi MA, Ammi M (2021) Adversarial multi-source transfer learning in healthcare: application to glucose prediction for diabetic people. Comput Methods Progr Biomed 199:105874","journal-title":"Comput Methods Progr Biomed"},{"key":"10956_CR62","first-page":"27249","volume":"34","author":"D Li","year":"2021","unstructured":"Li D, Zhang H (2021) Improved regularization and robustness for fine-tuning in neural networks. Adv Neural Inf Process Syst 34:27249\u201327262","journal-title":"Adv Neural Inf Process Syst"},{"key":"10956_CR63","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang M, Deng W (2018) Deep visual domain adaptation: a survey. Neurocomputing 312:135\u2013153","journal-title":"Neurocomputing"},{"key":"10956_CR64","doi-asserted-by":"crossref","unstructured":"You K, Long M, Cao Z, Wang J, Jordan MI (2019) Universal domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2720\u20132729","DOI":"10.1109\/CVPR.2019.00283"},{"key":"10956_CR65","doi-asserted-by":"crossref","unstructured":"Lee C-Y, Batra T, Baig MH, Ulbricht D (2019) Sliced wasserstein discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10285\u201310295","DOI":"10.1109\/CVPR.2019.01053"},{"key":"10956_CR66","doi-asserted-by":"crossref","unstructured":"Su J-C, Tsai Y-H, Sohn K, Liu B, Maji S, Chandraker M (2020) Active adversarial domain adaptation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 739\u2013748","DOI":"10.1109\/WACV45572.2020.9093390"},{"key":"10956_CR67","first-page":"914","volume":"34","author":"A Zhou","year":"2021","unstructured":"Zhou A, Levine S (2021) Bayesian adaptation for covariate shift. Adv Neural Inf Process Syst 34:914\u2013927","journal-title":"Adv Neural Inf Process Syst"},{"key":"10956_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108255","volume":"133","author":"R Lekshmi","year":"2024","unstructured":"Lekshmi R, Jose BR, Mathew J, Sanodiya RK (2024) Mnemonic: Multikernel contrastive domain adaptation for time-series classification. Eng Appl Artif Intell 133:108255","journal-title":"Eng Appl Artif Intell"},{"key":"10956_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765","volume":"65","author":"X Li","year":"2020","unstructured":"Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS (2020) Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Med Image Anal 65:101765","journal-title":"Med Image Anal"},{"issue":"3","key":"10956_CR70","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1002\/mrm.27106","volume":"80","author":"Y Han","year":"2018","unstructured":"Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC (2018) Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med 80(3):1189\u20131205","journal-title":"Magn Reson Med"},{"key":"10956_CR71","unstructured":"Wu Y, Winston E, Kaushik D, Lipton Z (2019) Domain adaptation with asymmetrically-relaxed distribution alignment. In: International conference on machine learning, PMLR, pp 6872\u20136881"},{"issue":"2","key":"10956_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"10956_CR73","doi-asserted-by":"crossref","unstructured":"Chen Y, Lu W, Qin X, Wang J, Xie X (2023) Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2023.3297103"},{"key":"10956_CR74","doi-asserted-by":"crossref","unstructured":"Xu Y, Fan H (2023) Feddk: Improving cyclic knowledge distillation for personalized healthcare federated learning. IEEE Access","DOI":"10.1109\/ACCESS.2023.3294812"},{"key":"10956_CR75","doi-asserted-by":"crossref","unstructured":"Huang C-j, Wang L, Han X (2023) Vertical federated knowledge transfer via representation distillation for healthcare collaboration networks. In: Proceedings of the ACM Web conference 2023, pp 4188\u20134199","DOI":"10.1145\/3543507.3583874"},{"key":"10956_CR76","unstructured":"Ding Q, Wu S, Sun H, Guo J, Xia S-T (2019) Adaptive regularization of labels, arXiv preprint arXiv:1908.05474"},{"key":"10956_CR77","doi-asserted-by":"crossref","unstructured":"Zhu S, Zhou C, Wang Y (2022) Super resolution reconstruction method for infrared images based on pseudo transferred features. Displays 102187","DOI":"10.1016\/j.displa.2022.102187"},{"key":"10956_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104115","volume":"128","author":"MA Morid","year":"2021","unstructured":"Morid MA, Borjali A, Del Fiol G (2021) A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med 128:104115","journal-title":"Comput Biol Med"},{"issue":"10","key":"10956_CR79","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM (2013) The cancer genome atlas pan-cancer analysis project. Nat Gen 45(10):1113\u20131120","journal-title":"Nat Gen"},{"issue":"12","key":"10956_CR80","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1056\/NEJMp1607591","volume":"375","author":"RL Grossman","year":"2016","unstructured":"Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, Staudt LM (2016) Toward a shared vision for cancer genomic data. New Engl J Med 375(12):1109\u20131112","journal-title":"New Engl J Med"},{"issue":"7403","key":"10956_CR81","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1038\/nature10983","volume":"486","author":"C Curtis","year":"2012","unstructured":"Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y et al (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403):346\u2013352","journal-title":"Nature"},{"issue":"2","key":"10956_CR82","doi-asserted-by":"publisher","DOI":"10.1016\/j.jik.2023.100313","volume":"8","author":"KT Chui","year":"2023","unstructured":"Chui KT, Arya V, Band SS, Alhalabi M, Liu RW, Chi HR (2023) Facilitating innovation and knowledge transfer between homogeneous and heterogeneous datasets: Generic incremental transfer learning approach and multidisciplinary studies. J Innov Knowl 8(2):100313","journal-title":"J Innov Knowl"},{"key":"10956_CR83","doi-asserted-by":"crossref","unstructured":"Talukder MA, Islam MM, Uddin MA, Akhter A, Pramanik MAJ, Aryal S, Almoyad MAA, Hasan KF, Moni MA (2023) An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Syst Appl 120534","DOI":"10.1016\/j.eswa.2023.120534"},{"key":"10956_CR84","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102572","volume":"141","author":"A Heidari","year":"2023","unstructured":"Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M (2023) A new lung cancer detection method based on the chest ct images using federated learning and blockchain systems. Artif Intell Med 141:102572","journal-title":"Artif Intell Med"},{"key":"10956_CR85","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107763","volume":"236","author":"A Raza","year":"2022","unstructured":"Raza A, Tran KP, Koehl L, Li S (2022) Designing ECG monitoring healthcare system with federated transfer learning and explainable AI. Knowl Based Syst 236:107763","journal-title":"Knowl Based Syst"},{"key":"10956_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106575","volume":"154","author":"MA Shamshiri","year":"2023","unstructured":"Shamshiri MA, Krzy\u017cak A, Kowal M, Korbicz J (2023) Compatible-domain transfer learning for breast cancer classification with limited annotated data. Comput Biol Med 154:106575","journal-title":"Comput Biol Med"},{"key":"10956_CR87","doi-asserted-by":"crossref","unstructured":"Kumari V, Ghosh R (2023) A magnification-independent method for breast cancer classification using transfer learning. Healthc Anal 100207","DOI":"10.1016\/j.health.2023.100207"},{"key":"10956_CR88","doi-asserted-by":"publisher","first-page":"25657","DOI":"10.1109\/ACCESS.2022.3150924","volume":"10","author":"S Mehmood","year":"2022","unstructured":"Mehmood S, Ghazal TM, Khan MA, Zubair M, Naseem MT, Faiz T, Ahmad M (2022) Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access 10:25657\u201325668","journal-title":"IEEE Access"},{"issue":"4","key":"10956_CR89","doi-asserted-by":"publisher","first-page":"339","DOI":"10.18280\/ts.360406","volume":"36","author":"T Sajja","year":"2019","unstructured":"Sajja T, Devarapalli R, Kalluri H (2019) Lung cancer detection based on CT scan images by using deep transfer learning. Traitement Signal 36(4):339\u2013344","journal-title":"Traitement Signal"},{"issue":"4","key":"10956_CR90","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","volume":"35","author":"Y Chen","year":"2020","unstructured":"Chen Y, Qin X, Wang J, Yu C, Gao W (2020) Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems 35(4):83\u201393","journal-title":"IEEE Intelligent Systems"},{"key":"10956_CR91","doi-asserted-by":"publisher","first-page":"27462","DOI":"10.1109\/ACCESS.2023.3257562","volume":"11","author":"YN Tan","year":"2023","unstructured":"Tan YN, Tinh VP, Lam PD, Nam NH, Khoa TA (2023) A transfer learning approach to breast cancer classification in a federated learning framework. IEEe Access 11:27462\u201327476","journal-title":"IEEe Access"},{"key":"10956_CR92","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00034-019-01246-3","volume":"39","author":"A Rehman","year":"2020","unstructured":"Rehman A, Naz S, Razzak MI, Akram F, Imran M (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ Syst Signal Process 39:757\u2013775","journal-title":"Circ Syst Signal Process"},{"key":"10956_CR93","doi-asserted-by":"crossref","unstructured":"Hosny KM, Kassem MA, Foaud MM (2018) Skin cancer classification using deep learning and transfer learning. In: 2018 9th Cairo international biomedical engineering conference (CIBEC), IEEE, pp 90\u201393","DOI":"10.1109\/CIBEC.2018.8641762"},{"key":"10956_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117695","volume":"205","author":"MA Talukder","year":"2022","unstructured":"Talukder MA, Islam MM, Uddin MA, Akhter A, Hasan KF, Moni MA (2022) Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Syst Appl 205:117695","journal-title":"Expert Syst Appl"},{"key":"10956_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105080","volume":"86","author":"S Kumbhare","year":"2023","unstructured":"Kumbhare S, Kathole AB, Shinde S (2023) Federated learning aided breast cancer detection with intelligent heuristic-based deep learning framework. Biomed Signal Process Control 86:105080","journal-title":"Biomed Signal Process Control"},{"key":"10956_CR96","doi-asserted-by":"crossref","unstructured":"Li X, Yang Z, Wang Q, Sun Y, Liu A (2023) Vision transformer for cell tumor image classification. In: 2023 3rd International conference on frontiers of electronics, information and computation technologies (ICFEICT), IEEE, pp 176\u2013180","DOI":"10.1109\/ICFEICT59519.2023.00039"},{"issue":"11","key":"10956_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100613","volume":"3","author":"Q Lyu","year":"2022","unstructured":"Lyu Q, Namjoshi SV, McTyre E, Topaloglu U, Barcus R, Chan MD, Cramer CK, Debinski W, Gurcan MN, Lesser GJ et al (2022) A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain mri images. Patterns 3(11):100613","journal-title":"Patterns"},{"issue":"11","key":"10956_CR98","doi-asserted-by":"publisher","first-page":"17437","DOI":"10.1007\/s11042-022-14107-0","volume":"82","author":"L Liu","year":"2023","unstructured":"Liu L, Fan K, Yang M (2023) Federated learning: a deep learning model based on resnet18 dual path for lung nodule detection. Multimed Tools Appl 82(11):17437\u201317450","journal-title":"Multimed Tools Appl"},{"key":"10956_CR99","doi-asserted-by":"crossref","unstructured":"Fang T (2018) A novel computer-aided lung cancer detection method based on transfer learning from googlenet and median intensity projections. In: 2018 IEEE international conference on computer and communication engineering technology (CCET), IEEE, pp 286\u2013290","DOI":"10.1109\/CCET.2018.8542189"},{"key":"10956_CR100","doi-asserted-by":"crossref","unstructured":"Tyagi S, Kushnure DT, Talbar SN (2023) An amalgamation of vision transformer with convolutional neural network for automatic lung tumor segmentation. Comput Med Imag Graph 102258","DOI":"10.1016\/j.compmedimag.2023.102258"},{"issue":"1","key":"10956_CR101","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TII.2021.3093715","volume":"19","author":"X Cai","year":"2021","unstructured":"Cai X, Lan Y, Zhang Z, Wen J, Cui Z, Zhang W (2021) A many-objective optimization based federal deep generation model for enhancing data processing capability in iot. IEEE Trans Ind Inf 19(1):561\u2013569","journal-title":"IEEE Trans Ind Inf"},{"key":"10956_CR102","doi-asserted-by":"crossref","unstructured":"Himeur Y, Elnour M, Fadli F, Meskin N, Petri I, Rezgui Y, Bensaali F, Amira A (2022) Next-generation energy systems for sustainable smart cities: Roles of transfer learning. Sustain Cit Soc 104059","DOI":"10.1016\/j.scs.2022.104059"},{"issue":"8","key":"10956_CR103","doi-asserted-by":"publisher","first-page":"10411","DOI":"10.1007\/s13369-022-06588-w","volume":"47","author":"A Slim","year":"2022","unstructured":"Slim A, Melouah A, Faghihi U, Sahib K (2022) Improving neural machine translation for low resource algerian dialect by transductive transfer learning strategy. Arabian J Sci Eng 47(8):10411\u201310418","journal-title":"Arabian J Sci Eng"},{"key":"10956_CR104","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"key":"10956_CR105","doi-asserted-by":"crossref","unstructured":"Sharifi-Noghabi H, Peng S, Zolotareva O, Collins CC, Ester M (2020) Aitl: adversarial inductive transfer learning with input and output space adaptation for pharmacogenomics. Bioinformatics 36(Supplement_1):i380\u2013i388","DOI":"10.1093\/bioinformatics\/btaa442"},{"key":"10956_CR106","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106895","volume":"221","author":"E Pay\u00e1","year":"2022","unstructured":"Pay\u00e1 E, Bori L, Colomer A, Meseguer M, Naranjo V (2022) Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques. Comput Methods Programs Biomed 221:106895","journal-title":"Comput Methods Programs Biomed"},{"key":"10956_CR107","doi-asserted-by":"crossref","unstructured":"Tokuoka Y, Suzuki S, Sugawara Y (2019) An inductive transfer learning approach using cycle-consistent adversarial domain adaptation with application to brain tumor segmentation. In: Proceedings of the 2019 6th international conference on biomedical and bioinformatics engineering, pp 44\u201348","DOI":"10.1145\/3375923.3375948"},{"key":"10956_CR108","doi-asserted-by":"crossref","unstructured":"Sarawgi U, Zulfikar W, Soliman N, Maes P (2020) Multimodal inductive transfer learning for detection of alzheimer\u2019s dementia and its severity, arXiv preprint arXiv:2009.00700","DOI":"10.21437\/Interspeech.2020-3137"},{"key":"10956_CR109","doi-asserted-by":"crossref","unstructured":"Khan MZ, Lee Y (2021) Dynamic inductive transfer learning with decision support feedback to optimize retina analysis. In: 2021 IEEE 9th International conference on healthcare informatics (ICHI), IEEE, pp 93\u2013100","DOI":"10.1109\/ICHI52183.2021.00025"},{"key":"10956_CR110","first-page":"145","volume":"2021","author":"N Agarwal","year":"2020","unstructured":"Agarwal N, Sondhi A, Chopra K, Singh G (2020) Transfer learning: survey and classification. Smart Innov Commun Comput Sci Proceed ICSICCS 2021:145\u2013155","journal-title":"Smart Innov Commun Comput Sci Proceed ICSICCS"},{"key":"10956_CR111","doi-asserted-by":"crossref","unstructured":"Chen Z, Mousavi M, de Sa VR (2022) Multi-subject unsupervised transfer with weighted subspace alignment for common spatial patterns. In: 2022 10th International winter conference on brain-computer interface (BCI), IEEE, pp 1\u20136","DOI":"10.1109\/BCI53720.2022.9735012"},{"key":"10956_CR112","doi-asserted-by":"crossref","unstructured":"Chen Y-H, Chen W-Y, Chen Y-T, Tsai B-C, Frank Wang Y-C, Sun M (2017) No more discrimination: Cross city adaptation of road scene segmenters. In: Proceedings of the IEEE international conference on computer vision, pp 1992\u20132001","DOI":"10.1109\/ICCV.2017.220"},{"key":"10956_CR113","doi-asserted-by":"crossref","unstructured":"Ahn E, Kumar A, Feng D, Fulham M, Kim J (2019) Unsupervised deep transfer feature learning for medical image classification. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), IEEE, pp 1915\u20131918","DOI":"10.1109\/ISBI.2019.8759275"},{"key":"10956_CR114","first-page":"1","volume":"70","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Zhang Q, Yu X, Sun C, Wang S, Yan R, Chen X (2021) Applications of unsupervised deep transfer learning to intelligent fault diagnosis: a survey and comparative study. IEEE Trans Instrum Meas 70:1\u201328","journal-title":"IEEE Trans Instrum Meas"},{"key":"10956_CR115","doi-asserted-by":"crossref","unstructured":"Yang L, Lu B, Zhou Q, Su P (2023) Unsupervised domain adaptation via re-weighted transfer subspace learning with inter-class sparsity. Knowl Based Syst 110277","DOI":"10.1016\/j.knosys.2023.110277"},{"key":"10956_CR116","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103491","volume":"222","author":"J Zhou","year":"2022","unstructured":"Zhou J, Komuro T (2022) An asymmetrical-structure auto-encoder for unsupervised representation learning of skeleton sequences. Comput Vision Image Underst 222:103491","journal-title":"Comput Vision Image Underst"},{"key":"10956_CR117","doi-asserted-by":"crossref","unstructured":"Wang F, Jiao L, Pan Q (2021) A survey on unsupervised transfer clustering. In: 2021 40th Chinese control conference (CCC), IEEE, pp 7361\u20137365","DOI":"10.23919\/CCC52363.2021.9549617"},{"key":"10956_CR118","unstructured":"Roy AG, Siddiqui S, P\u00f6lsterl S, Navab N, Wachinger C (2019) Braintorrent: A peer-to-peer environment for decentralized federated learning, arXiv preprint arXiv:1905.06731"},{"key":"10956_CR119","unstructured":"Zhang X, Yin W, Hong M, Chen T (2020) Hybrid federated learning: Algorithms and implementation, arXiv preprint arXiv:2012.12420"},{"issue":"7","key":"10956_CR120","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman S, Tout H, Ould-Slimane H, Mourad A, Talhi C, Guizani M (2020) A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Int Things J 8(7):5476\u20135497","journal-title":"IEEE Int Things J"},{"issue":"2","key":"10956_CR121","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/TNSM.2012.031512.110176","volume":"9","author":"F Wuhib","year":"2012","unstructured":"Wuhib F, Stadler R, Spreitzer M (2012) A gossip protocol for dynamic resource management in large cloud environments. IEEE Trans Netw Serv Manag 9(2):213\u2013225","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"10956_CR122","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s10796-011-9304-2","volume":"14","author":"JH Weber-Jahnke","year":"2012","unstructured":"Weber-Jahnke JH, Obry C (2012) Protecting privacy during peer-to-peer exchange of medical documents. Inf Syst Front 14:87\u2013104","journal-title":"Inf Syst Front"},{"issue":"1","key":"10956_CR123","doi-asserted-by":"publisher","first-page":"2772","DOI":"10.1038\/s41598-022-05215-w","volume":"12","author":"H Li","year":"2022","unstructured":"Li H, Li M (2022) Patent data access control and protection using blockchain technology. Sci Rep 12(1):2772","journal-title":"Sci Rep"},{"key":"10956_CR124","unstructured":"Guo Y, Sun Y, Hu R, Gong Y (2022) Hybrid local sgd for federated learning with heterogeneous communications. In: International conference on learning representations"},{"key":"10956_CR125","unstructured":"Mammen PM (2021) Federated learning: opportunities and challenges, arXiv preprint arXiv:2101.05428"},{"issue":"11","key":"10956_CR126","doi-asserted-by":"publisher","first-page":"6103","DOI":"10.1109\/TNNLS.2021.3072238","volume":"33","author":"B Gu","year":"2021","unstructured":"Gu B, Xu A, Huo Z, Deng C, Huang H (2021) Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning. IEEE Trans Neural Netw Learn Syst 33(11):6103\u20136115","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10956_CR127","doi-asserted-by":"crossref","unstructured":"Das A, Patterson S (2021) Multi-tier federated learning for vertically partitioned data. In: ICASSP 2021-2021 IEEE International conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 3100\u20133104","DOI":"10.1109\/ICASSP39728.2021.9415026"},{"key":"10956_CR128","unstructured":"Yang S, Ren B, Zhou X, Liu L (2019) Parallel distributed logistic regression for vertical federated learning without third-party coordinator, arXiv preprint arXiv:1911.09824"},{"key":"10956_CR129","unstructured":"Wei K, Li J, Ma C, Ding M, Wei S, Wu F, Chen G, Ranbaduge T (2022) Vertical federated learning: Challenges, methodologies and experiments, arXiv preprint arXiv:2202.04309"},{"key":"10956_CR130","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1016\/j.procs.2023.01.127","volume":"218","author":"S Ambesange","year":"2023","unstructured":"Ambesange S, Annappa B, Koolagudi SG (2023) Simulating federated transfer learning for lung segmentation using modified UNet model. Proced Comput Sci 218:1485\u20131496","journal-title":"Proced Comput Sci"},{"key":"10956_CR131","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110413","volume":"198","author":"R Wang","year":"2023","unstructured":"Wang R, Yan F, Yu L, Shen C, Hu X, Chen J (2023) A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis. Mech Syst Signal Process 198:110413","journal-title":"Mech Syst Signal Process"},{"key":"10956_CR132","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/j.jmsy.2023.05.006","volume":"68","author":"X Li","year":"2023","unstructured":"Li X, Zhang C, Li X, Zhang W (2023) Federated transfer learning in fault diagnosis under data privacy with target self-adaptation. J Manuf Syst 68:523\u2013535","journal-title":"J Manuf Syst"},{"key":"10956_CR133","unstructured":"Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y (2020) Federated learning with matched averaging. https:\/\/openreview.net\/forum?id=BkluqlSFDS"},{"key":"10956_CR134","doi-asserted-by":"crossref","unstructured":"Ek S, Portet F, Lalanda P, Vega G (2020) Evaluation of federated learning aggregation algorithms: application to human activity recognition. In: Adjunct proceedings of the 2020 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2020 ACM international symposium on wearable computers, pp 638\u2013643","DOI":"10.1145\/3410530.3414321"},{"key":"10956_CR135","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, PMLR, pp 1273\u20131282"},{"key":"10956_CR136","doi-asserted-by":"crossref","unstructured":"Shin W, Shin J (2022) Fedvar: Federated learning algorithm with weight variation in clients. In: 2022 37th international technical conference on circuits\/systems, computers and communications (ITC-CSCC), IEEE, pp 1\u20134","DOI":"10.1109\/ITC-CSCC55581.2022.9894899"},{"key":"10956_CR137","doi-asserted-by":"crossref","unstructured":"Thonglek K, Takahashi K, Ichikawa K, Iida H, Nakasan C (2020) Federated learning of neural network models with heterogeneous structures. In: 2020 19th IEEE International conference on machine learning and applications (ICMLA), IEEE, pp 735\u2013740","DOI":"10.1109\/ICMLA51294.2020.00120"},{"key":"10956_CR138","unstructured":"Li X, Huang K, Yang W, Wang S, Zhang Z (2020) On the convergence of fedavg on non-iid data. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=HJxNAnVtDS"},{"key":"10956_CR139","unstructured":"Casella B, Esposito R, Cavazzoni C, Aldinucci M (2023) Benchmarking fedavg and fedcurv for image classification tasks, arXiv preprint arXiv:2303.17942"},{"key":"10956_CR140","first-page":"12052","volume":"34","author":"X Gu","year":"2021","unstructured":"Gu X, Huang K, Zhang J, Huang L (2021) Fast federated learning in the presence of arbitrary device unavailability. Adv Neural Inf Process Syst 34:12052\u201312064","journal-title":"Adv Neural Inf Process Syst"},{"key":"10956_CR141","first-page":"11","volume":"4","author":"M Ahmed","year":"2023","unstructured":"Ahmed M, Afreen N, Ahmed M, Sameer M, Ahamed J (2023) An inception v3 approach for malware classification using machine learning and transfer learning. Int J Intell Netw 4:11\u201318","journal-title":"Int J Intell Netw"},{"key":"10956_CR142","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.future.2020.08.015","volume":"114","author":"Z Liu","year":"2021","unstructured":"Liu Z, Yang C, Huang J, Liu S, Zhuo Y, Lu X (2021) Deep learning framework based on integration of S-Mask R-CNN and inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Future Gen Comput Syst 114:358\u2013367","journal-title":"Future Gen Comput Syst"},{"key":"10956_CR143","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106311","volume":"93","author":"N Dong","year":"2020","unstructured":"Dong N, Zhao L, Wu C-H, Chang J-F (2020) Inception v3 based cervical cell classification combined with artificially extracted features. Appl Soft Comput 93:106311","journal-title":"Appl Soft Comput"},{"key":"10956_CR144","doi-asserted-by":"crossref","unstructured":"Demir A, Yilmaz F, Kose O (2019) Early detection of skin cancer using deep learning architectures: resnet-101 and inception-v3. In: 2019 medical technologies congress (TIPTEKNO), IEEE, pp 1\u20134","DOI":"10.1109\/TIPTEKNO47231.2019.8972045"},{"key":"10956_CR145","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.procs.2023.01.018","volume":"218","author":"S Sharma","year":"2023","unstructured":"Sharma S, Guleria K (2023) A deep learning based model for the detection of pneumonia from chest X-ray images using VGG-16 and neural networks. Proced Comput Sci 218:357\u2013366","journal-title":"Proced Comput Sci"},{"key":"10956_CR146","volume":"35","author":"DF Santos-Bustos","year":"2022","unstructured":"Santos-Bustos DF, Nguyen BM, Espitia HE (2022) Towards automated eye cancer classification via VGG and ResNet networks using transfer learning. Eng Sci Technol Int J 35:101214","journal-title":"Eng Sci Technol Int J"},{"key":"10956_CR147","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2022.100588","volume":"24","author":"R Pandian","year":"2022","unstructured":"Pandian R, Vedanarayanan V, Kumar DR, Rajakumar R (2022) Detection and classification of lung cancer using cnn and google net. Meas Sens 24:100588","journal-title":"Meas Sens"},{"key":"10956_CR148","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107622","volume":"205","author":"D Wu","year":"2023","unstructured":"Wu D, Ying Y, Zhou M, Pan J, Cui D (2023) Improved ResNet-50 deep learning algorithm for identifying chicken gender. Comput Electron Agrics 205:107622","journal-title":"Comput Electron Agrics"},{"key":"10956_CR149","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.procs.2021.01.025","volume":"179","author":"D Sarwinda","year":"2021","unstructured":"Sarwinda D, Paradisa RH, Bustamam A, Anggia P (2021) Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Proced Comput Sci 179:423\u2013431","journal-title":"Proced Comput Sci"},{"issue":"2","key":"10956_CR150","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.gltp.2021.08.027","volume":"2","author":"AV Ikechukwu","year":"2021","unstructured":"Ikechukwu AV, Murali S, Deepu R, Shivamurthy R (2021) ResNet-50 vs VGG-19 vs training from scratch: a comparative analysis of the segmentation and classification of pneumonia from chest x-ray images. Glob Trans Proceed 2(2):375\u2013381","journal-title":"Glob Trans Proceed"},{"key":"10956_CR151","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104564","volume":"82","author":"E B\u00fct\u00fcn","year":"2023","unstructured":"B\u00fct\u00fcn E, U\u00e7an M, Kaya M (2023) Automatic detection of cancer metastasis in lymph node using deep learning. Biomed Signal Process Control 82:104564","journal-title":"Biomed Signal Process Control"},{"key":"10956_CR152","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"10956_CR153","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106170","volume":"150","author":"A Panthakkan","year":"2022","unstructured":"Panthakkan A, Anzar S, Jamal S, Mansoor W (2022) Concatenated Xception-resnet50-a novel hybrid approach for accurate skin cancer prediction. Comput Biol Med 150:106170","journal-title":"Comput Biol Med"},{"key":"10956_CR154","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.procs.2022.12.403","volume":"218","author":"C Upasana","year":"2023","unstructured":"Upasana C, Tewari AS, Singh JP (2023) An attention-based pneumothorax classification using modified Xception model. Proced Comput Sci 218:74\u201382","journal-title":"Proced Comput Sci"},{"issue":"1","key":"10956_CR155","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.icte.2021.11.010","volume":"8","author":"S Sharma","year":"2022","unstructured":"Sharma S, Kumar S (2022) The Xception model: a potential feature extractor in breast cancer histology images classification. ICT Express 8(1):101\u2013108","journal-title":"ICT Express"},{"key":"10956_CR156","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2021.103894","volume":"119","author":"Y Liu","year":"2021","unstructured":"Liu Y, Miao C, Ji J, Li X (2021) MMF: A multi-scale MobileNet based fusion method for infrared and visible image. Infrared Phys Technol 119:103894","journal-title":"Infrared Phys Technol"},{"key":"10956_CR157","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1016\/j.procs.2023.01.164","volume":"218","author":"R Mothkur","year":"2023","unstructured":"Mothkur R, Veerappa B (2023) Classification of lung cancer using lightweight deep neural networks. Proced Comput Sci 218:1869\u20131877","journal-title":"Proced Comput Sci"},{"key":"10956_CR158","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.875761","volume":"12","author":"H Zhao","year":"2022","unstructured":"Zhao H, Su Y, Wang M, Lyu Z, Xu P, Jiao Y, Zhang L, Han W, Tian L, Fu P (2022) The machine learning model for distinguishing pathological subtypes of non-small cell lung cancer. Front Oncol 12:875761","journal-title":"Front Oncol"},{"issue":"10s","key":"10956_CR159","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM comput Surv (CSUR) 54(10s):1\u201341","journal-title":"ACM comput Surv (CSUR)"},{"issue":"1","key":"10956_CR160","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.imed.2022.07.002","volume":"3","author":"K He","year":"2023","unstructured":"He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, Gao Y, Wang Q, Zhang J, Shen D (2023) Transformers in medical image analysis. Intell Med 3(1):59\u201378","journal-title":"Intell Med"},{"key":"10956_CR161","doi-asserted-by":"crossref","unstructured":"Zhai X, Kolesnikov A, Houlsby N, Beyer L (2022) Scaling vision transformers, in: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 12104\u201312113","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"10956_CR162","doi-asserted-by":"crossref","unstructured":"Wang P, Yang Q, He Z, Yuan Y (2023) Vision transformers in multi-modal brain tumor mri segmentation: A review. Meta Radiol 100004","DOI":"10.1016\/j.metrad.2023.100004"},{"key":"10956_CR163","doi-asserted-by":"crossref","unstructured":"Andrade-Miranda G, Jaouen V, Bourbonne V, Lucia F, Visvikis D, Conze P-H (2022) Pure versus hybrid transformers for multi-modal brain tumor segmentation: a comparative study. In: 2022 IEEE international conference on image processing (ICIP), IEEE, pp 1336\u20131340","DOI":"10.1109\/ICIP46576.2022.9897658"},{"key":"10956_CR164","doi-asserted-by":"crossref","unstructured":"Xu X, Prasanna P (2022) Brain cancer survival prediction on treatment-na\u00efve mri using deep anchor attention learning with vision transformer. In: 2022 IEEE 19th International symposium on biomedical imaging (ISBI), IEEE, pp 1\u20135","DOI":"10.1109\/ISBI52829.2022.9761515"},{"key":"10956_CR165","doi-asserted-by":"crossref","unstructured":"Xie J, Wu Z, Zhu R, Zhu H (2021) Melanoma detection based on swin transformer and simam. In: 2021 IEEE 5th Information technology, networking, electronic and automation control conference (itnec), vol 5, ieee, pp 1517\u20131521","DOI":"10.1109\/ITNEC52019.2021.9587071"},{"key":"10956_CR166","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"6","key":"10956_CR167","doi-asserted-by":"publisher","first-page":"797","DOI":"10.3390\/brainsci12060797","volume":"12","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Zhang Y, Lin X, Dong J, Cheng T, Liang J (2022) SwinBTS: a method for 3D multimodal brain tumor segmentation using swin transformer. Brain Sci 12(6):797","journal-title":"Brain Sci"},{"key":"10956_CR168","doi-asserted-by":"crossref","unstructured":"Karthik R, Hussain S, George TT, Mishra R (2023) A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and swin transformer. Ecol Inf 102302","DOI":"10.1016\/j.ecoinf.2023.102302"},{"key":"10956_CR169","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110393","volume":"267","author":"A Iqbal","year":"2023","unstructured":"Iqbal A, Sharif M (2023) BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images. Knowl Based Syst 267:110393","journal-title":"Knowl Based Syst"},{"key":"10956_CR170","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119452","volume":"216","author":"U Zidan","year":"2023","unstructured":"Zidan U, Gaber MM, Abdelsamea MM (2023) Swincup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer. Expert Syst Appl 216:119452","journal-title":"Expert Syst Appl"},{"key":"10956_CR171","doi-asserted-by":"crossref","unstructured":"Masood A, Naseem U, Razzak I (2023) Multi-scale swin transformer enabled automatic detection and segmentation of lung metastases using CT images. In: 2023 IEEE 20th international symposium on biomedical imaging (ISBI), IEEE, pp 1\u20135","DOI":"10.1109\/ISBI53787.2023.10230663"},{"issue":"1","key":"10956_CR172","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s13748-023-00300-1","volume":"12","author":"P Zou","year":"2023","unstructured":"Zou P, Wu J-S (2023) Swine-unet3+: swin transformer encoder network for medical image segmentation. Progress Artif Intell 12(1):99\u2013105","journal-title":"Progress Artif Intell"},{"key":"10956_CR173","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: Transformers make strong encoders for medical image segmentation, arXiv preprint arXiv:2102.04306"},{"key":"10956_CR174","doi-asserted-by":"crossref","unstructured":"Castro R, Ramos L, Rom\u00e1n S, Bermeo M, Crespo A, Cuenca E (2022) U-net vs. transunet: Performance comparison in medical image segmentation. In: International conference on applied technologies, Springer, pp 212\u2013226","DOI":"10.1007\/978-3-031-24985-3_16"},{"key":"10956_CR175","doi-asserted-by":"publisher","first-page":"1060798","DOI":"10.3389\/fpubh.2022.1060798","volume":"10","author":"H Wang","year":"2022","unstructured":"Wang H, Zhu H, Ding L (2022) Accurate classification of lung nodules on CT images using the TransuNet. Front Publ Health 10:1060798","journal-title":"Front Publ Health"},{"key":"10956_CR176","doi-asserted-by":"crossref","unstructured":"Chen X, Yang L (2022) Brain tumor segmentation based on cbam-transunet. In: Proceedings of the 1st ACM workshop on mobile and wireless sensing for smart healthcare, p. 33\u201338","DOI":"10.1145\/3556551.3561192"},{"key":"10956_CR177","doi-asserted-by":"crossref","unstructured":"Wang E, Hu Y, Yang X, Tian X (2022) Transunet with attention mechanism for brain tumor segmentation on mr images. In: 2022 IEEE international conference on artificial intelligence and computer applications (ICAICA), IEEE, pp 573\u2013577","DOI":"10.1109\/ICAICA54878.2022.9844551"},{"key":"10956_CR178","unstructured":"Chen J, Chen J, Zhou Z, Li B, Yuille A, Lu Y (2021) Mt-transunet: Mediating multi-task tokens in transformers for skin lesion segmentation and classification, arXiv preprint arXiv:2112.01767"},{"issue":"21","key":"10956_CR179","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac97d9","volume":"67","author":"P Foley","year":"2022","unstructured":"Foley P, Sheller MJ, Edwards B, Pati S, Riviera W, Sharma M, Moorthy PN, Wang S-H, Martin J, Mirhaji P et al (2022) OpenFL: the open federated learning library. Phys Med Biol 67(21):214001","journal-title":"Phys Med Biol"},{"key":"10956_CR180","unstructured":"Reina GA, Gruzdev A, Foley P, Perepelkina O, Sharma M, Davidyuk I, Trushkin I, Radionov M, Mokrov A, Agapov D, et al (2021) Openfl: An open-source framework for federated learning, arXiv preprint arXiv:2105.06413"},{"key":"10956_CR181","unstructured":"INRIA, An open-source federated learning framework, Fed-BioMed"},{"key":"10956_CR182","doi-asserted-by":"crossref","unstructured":"Silva S, Altmann A, Gutman B, Lorenzi M (2020) Fed-biomed: a general open-source frontend framework for federated learning in healthcare. In: Domain adaptation and representation transfer, and distributed and collaborative learning: second MICCAI workshop, DART 2020, and first MICCAI workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4\u20138, 2020, Proceedings 2, Springer, pp 201\u2013210","DOI":"10.1007\/978-3-030-60548-3_20"},{"key":"10956_CR183","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.procs.2022.12.227","volume":"217","author":"M Khan","year":"2023","unstructured":"Khan M, Glavin FG, Nickles M (2023) Federated learning as a privacy solution-an overview. Proced Comput Sci 217:316\u2013325","journal-title":"Proced Comput Sci"},{"key":"10956_CR184","doi-asserted-by":"crossref","unstructured":"Jab\u0142ecki P, \u015alazyk F, Malawski M (2021) Federated learning in the cloud for analysis of medical images-experience with open source frameworks. In: Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and Secure and Privacy-Preserving Machine Learning: 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, first workshop, LL-COVID19 2021, and first workshop and tutorial, PPML 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings 2, Springer, pp. 111\u2013119","DOI":"10.1007\/978-3-030-90874-4_11"},{"key":"10956_CR185","unstructured":"Cremonesi F, Vesin M, Cansiz S, Bouillard Y, Balelli I, Innocenti L, Silva S, Ayed S-S, Taiello R, Kameni L, et al (2023) Fed-biomed: Open, transparent and trusted federated learning for real-world healthcare applications, arXiv preprint arXiv:2304.12012"},{"key":"10956_CR186","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Yin H, Molchanov P, Myronenko A, Li W, Dogra P, Feng A, Flores MG, Kautz J, Xu D et al (2023) Do gradient inversion attacks make federated learning unsafe? IEEE Transactions on Medical Imaging","DOI":"10.1109\/TMI.2023.3239391"},{"key":"10956_CR187","doi-asserted-by":"crossref","unstructured":"Nair AK, Raj ED, Sahoo J (2023) A robust analysis of adversarial attacks on federated learning environments. Comput Stand Interfac 103723","DOI":"10.1016\/j.csi.2023.103723"},{"key":"10956_CR188","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.ins.2018.10.024","volume":"476","author":"C Zhao","year":"2019","unstructured":"Zhao C, Zhao S, Zhao M, Chen Z, Gao C-Z, Li H, Tan Y-A (2019) Secure multi-party computation: theory, practice and applications. Inf Sci 476:357\u2013372","journal-title":"Inf Sci"},{"key":"10956_CR189","doi-asserted-by":"crossref","unstructured":"Naehrig M, Lauter K, Vaikuntanathan V (2011) Can homomorphic encryption be practical?. In: Proceedings of the 3rd ACM workshop on cloud computing security workshop, pp 113\u2013124","DOI":"10.1145\/2046660.2046682"},{"key":"10956_CR190","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103270","volume":"130","author":"P Gupta","year":"2023","unstructured":"Gupta P, Yadav K, Gupta BB, Alazab M, Gadekallu TR (2023) A novel data poisoning attack in federated learning based on inverted loss function. Comput Secur 130:103270","journal-title":"Comput Secur"},{"key":"10956_CR191","doi-asserted-by":"crossref","unstructured":"Kasyap H, Tripathy S (2023) Beyond data poisoning in federated learning. Expert Syst Appl 121192","DOI":"10.1016\/j.eswa.2023.121192"},{"key":"10956_CR192","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.ins.2023.02.025","volume":"630","author":"M Yang","year":"2023","unstructured":"Yang M, Cheng H, Chen F, Liu X, Wang M, Li X (2023) Model poisoning attack in differential privacy-based federated learning. Inf Sci 630:158\u2013172","journal-title":"Inf Sci"},{"key":"10956_CR193","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100875","volume":"23","author":"H Yang","year":"2023","unstructured":"Yang H, Gu D, He J (2023) Demac: towards detecting model poisoning attacks in federated learning system. Int Things 23:100875","journal-title":"Int Things"},{"key":"10956_CR194","doi-asserted-by":"crossref","unstructured":"Tolpegin V, Truex S, Gursoy ME, Liu L (2020) Data poisoning attacks against federated learning systems. In: Computer Security\u2013ESORICS 2020: 25th European Symposium on Research in Computer Security, ESORICS 2020, Guildford, UK, September 14\u201318, 2020, Proceedings, Part I 25, Springer, pp. 480\u2013501","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"10956_CR195","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez S\u00e1nchez PM, Huertas Celdr\u00e1n A, Buend\u00eda Rubio JR, Bovet G, Mart\u00ednez P\u00e9rez G (2023) Robust federated learning for execution time-based device model identification under label-flipping attack. Clust Comput 1\u201312","DOI":"10.1007\/s10586-022-03949-w"},{"key":"10956_CR196","unstructured":"Jebreel NM, Domingo-Ferrer J, S\u00e1nchez D, Blanco-Justicia A (2022) Defending against the label-flipping attack in federated learning, arXiv preprint arXiv:2207.01982"},{"key":"10956_CR197","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/TIFS.2023.3249568","volume":"18","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Zhang W, Chen Y (2023) Data quality detection mechanism against label flipping attacks in federated learning. IEEE Trans Inf Foren Secur 18:1625\u20131637","journal-title":"IEEE Trans Inf Foren Secur"},{"key":"10956_CR198","doi-asserted-by":"crossref","unstructured":"Li D, Wong WE, Wang W, Yao Y, Chau M (2021) Detection and mitigation of label-flipping attacks in federated learning systems with KPCA and K-means. In: 2021 8th international conference on dependable systems and their applications (DSA), IEEE, pp 551\u2013559","DOI":"10.1109\/DSA52907.2021.00081"},{"key":"10956_CR199","unstructured":"Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V (2020) How to backdoor federated learning. In: International conference on artificial intelligence and statistics, PMLR, pp 2938\u20132948"},{"key":"10956_CR200","doi-asserted-by":"crossref","unstructured":"Zhu C, Zhang J, Sun X, Chen B, Meng W (2023) ADFL: Defending backdoor attacks in federated learning via adversarial distillation. Comput Secur 103366","DOI":"10.1016\/j.cose.2023.103366"},{"key":"10956_CR201","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhai D-H, Xia Y (2023) SCFL: Mitigating backdoor attacks in federated learning based on svd and clustering. Comput Secur 103414","DOI":"10.1016\/j.cose.2023.103414"},{"key":"10956_CR202","unstructured":"Sun Z, Kairouz P, Suresh AT, McMahan HB (2019) Can you really backdoor federated learning?, arXiv preprint arXiv:1911.07963"},{"issue":"10","key":"10956_CR203","doi-asserted-by":"publisher","first-page":"5479","DOI":"10.3390\/ijerph18105479","volume":"18","author":"M Dildar","year":"2021","unstructured":"Dildar M, Akram S, Irfan M, Khan HU, Ramzan M, Mahmood AR, Alsaiari SA, Saeed AHM, Alraddadi MO, Mahnashi MH (2021) Skin cancer detection: a review using deep learning techniques. Int J Environ Res publ Health 18(10):5479","journal-title":"Int J Environ Res publ Health"},{"key":"10956_CR204","doi-asserted-by":"crossref","unstructured":"Chang J, Yu B, Saltzman WM, Girardi M (2023) Nanoparticles as a therapeutic delivery system for skin cancer prevention and treatment. JID Innovations 100197","DOI":"10.1016\/j.xjidi.2023.100197"},{"key":"10956_CR205","doi-asserted-by":"crossref","unstructured":"Ferguson J, Eleftheriadou V, Nesnas J (2023) Risk of melanoma and non-melanoma skin cancer in people with vitiligo: Uk population-based cohort study. J Invest Dermatol","DOI":"10.1016\/j.jid.2023.04.013"},{"issue":"5","key":"10956_CR206","doi-asserted-by":"publisher","first-page":"2145","DOI":"10.3390\/app11052145","volume":"11","author":"MA Hashmani","year":"2021","unstructured":"Hashmani MA, Jameel SM, Rizvi SSH, Shukla S (2021) An adaptive federated machine learning-based intelligent system for skin disease detection: a step toward an intelligent dermoscopy device. Appl Sci 11(5):2145","journal-title":"Appl Sci"},{"key":"10956_CR207","doi-asserted-by":"publisher","first-page":"114822","DOI":"10.1109\/ACCESS.2020.3003890","volume":"8","author":"MA Kassem","year":"2020","unstructured":"Kassem MA, Hosny KM, Fouad MM (2020) Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access 8:114822\u2013114832","journal-title":"IEEE Access"},{"key":"10956_CR208","doi-asserted-by":"crossref","unstructured":"Kondaveeti HK, Edupuganti P (2020) Skin cancer classification using transfer learning. In: 2020 IEEE international conference on advent trends in multidisciplinary research and innovation (ICATMRI), IEEE, pp 1\u20134","DOI":"10.1109\/ICATMRI51801.2020.9398388"},{"key":"10956_CR209","volume":"5","author":"MS Ali","year":"2021","unstructured":"Ali MS, Miah MS, Haque J, Rahman MM, Islam MK (2021) An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach Learn Appl 5:100036","journal-title":"Mach Learn Appl"},{"key":"10956_CR210","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s10552-014-0377-3","volume":"25","author":"JA Dumalaon-Canaria","year":"2014","unstructured":"Dumalaon-Canaria JA, Hutchinson AD, Prichard I, Wilson C (2014) What causes breast cancer? A systematic review of causal attributions among breast cancer survivors and how these compare to expert-endorsed risk factors. Cancer Causes & Control 25:771\u2013785","journal-title":"Cancer Causes & Control"},{"key":"10956_CR211","unstructured":"Roth HR, Chang K, Singh P, Neumark N, Li W, Gupta V, Gupta S, Qu L, Ihsani A, Bizzo BC, et al (2020) Federated learning for breast density classification: A real-world implementation, In: Domain adaptation and representation transfer, and distributed and collaborative learning: second MICCAI workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4\u20138, 2020, Proceedings 2, Springer, pp 181\u2013191"},{"key":"10956_CR212","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107318","volume":"229","author":"A Jim\u00e9nez-S\u00e1nchez","year":"2023","unstructured":"Jim\u00e9nez-S\u00e1nchez A, Tardy M, Ballester MAG, Mateus D, Piella G (2023) Memory-aware curriculum federated learning for breast cancer classification. Comput Methods Programs Biomed 229:107318","journal-title":"Comput Methods Programs Biomed"},{"key":"10956_CR213","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"S Khan","year":"2019","unstructured":"Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1\u20136","journal-title":"Pattern Recognit Lett"},{"key":"10956_CR214","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104003","volume":"126","author":"I Pacal","year":"2020","unstructured":"Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U (2020) A comprehensive review of deep learning in colon cancer. Comput Biol Med 126:104003","journal-title":"Comput Biol Med"},{"issue":"1","key":"10956_CR215","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14740\/gr1239","volume":"13","author":"M Ahmed","year":"2020","unstructured":"Ahmed M (2020) Colon cancer: a clinician\u2019s perspective in 2019. Gastroenterol Res 13(1):1\u201310","journal-title":"Gastroenterol Res"},{"key":"10956_CR216","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104283","volume":"80","author":"M Murugesan","year":"2023","unstructured":"Murugesan M, Arieth RM, Balraj S, Nirmala R (2023) Colon cancer stage detection in colonoscopy images using yolov3 msf deep learning architecture. Biomed Signal Process Control 80:104283","journal-title":"Biomed Signal Process Control"},{"key":"10956_CR217","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1007\/s11548-019-02004-1","volume":"14","author":"N Gessert","year":"2019","unstructured":"Gessert N, Bengs M, Wittig L, Dr\u00f6mann D, Keck T, Schlaefer A, Ellebrecht DB (2019) Deep transfer learning methods for colon cancer classification in confocal laser microscopy images. Int J Comput Assist Radiol Surg 14:1837\u20131845","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10956_CR218","doi-asserted-by":"crossref","unstructured":"Hormuth DA II, Farhat M, Christenson C, Curl B, Quarles CC, Chung C, Yankeelov TE (2022) Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 114367","DOI":"10.1016\/j.addr.2022.114367"},{"key":"10956_CR219","doi-asserted-by":"crossref","unstructured":"Al Mamun A, Uddin MS, Perveen A, Jha NK, Alghamdi BS, Jeandet P, Zhang H-J, Ashraf GM (2022) Inflammation-targeted nanomedicine against brain cancer: From design strategies to future developments. In: Seminars in Cancer Biology, Elsevier","DOI":"10.1016\/j.semcancer.2022.08.007"},{"key":"10956_CR220","doi-asserted-by":"crossref","unstructured":"Yi L, Zhang J, Zhang R, Shi J, Wang G, Liu X (2020) Su-net: an efficient encoder-decoder model of federated learning for brain tumor segmentation. In: Artificial Neural Networks and Machine Learning\u2013ICANN 2020: 29th International conference on artificial neural networks, Bratislava, Slovakia, September 15\u201318, 2020, Proceedings, Part I, Springer, pp 761\u2013773","DOI":"10.1007\/978-3-030-61609-0_60"},{"key":"10956_CR221","doi-asserted-by":"crossref","unstructured":"Jacob V, Sagar G, Goura K, Pedalanka PS (2023) Brain tumor classification based on deep cnn and modified butterfly optimization algorithm. Comput Methods Biomech Biomed Eng Imag Vis 1\u201312","DOI":"10.1080\/21681163.2023.2219754"},{"issue":"1","key":"10956_CR222","doi-asserted-by":"publisher","first-page":"45","DOI":"10.5114\/wo.2021.103829","volume":"25","author":"KC Thandra","year":"2021","unstructured":"Thandra KC, Barsouk A, Saginala K, Aluru JS, Barsouk A (2021) Epidemiology of lung cancer. Contemp Oncol \/Wsp\u00f3\u0142czesna Onkol 25(1):45\u201352","journal-title":"Contemp Oncol \/Wsp\u00f3\u0142czesna Onkol"},{"issue":"1","key":"10956_CR223","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41572-020-00235-0","volume":"7","author":"CM Rudin","year":"2021","unstructured":"Rudin CM, Brambilla E, Faivre-Finn C, Sage J (2021) Small-cell lung cancer. Nat Rev Dis Prim 7(1):3","journal-title":"Nat Rev Dis Prim"},{"key":"10956_CR224","doi-asserted-by":"crossref","unstructured":"Ayekai BJ, Wenyu C, Hailemichael MT, Fiasam LD, Kwaku AV, Agbley F, Ayivi W, Sam F, Danso JM, Kulevome D, et al (2022) Federated lung cancer prediction using histopathological medical images. In: 2022 19th international computer conference on wavelet active media technology and information processing (ICCWAMTIP), IEEE, pp 1\u20136","DOI":"10.1109\/ICCWAMTIP56608.2022.10016519"},{"key":"10956_CR225","unstructured":"Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic), arXiv preprint arXiv:1902.03368"},{"issue":"1","key":"10956_CR226","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5(1):1\u20139","journal-title":"Sci Data"},{"key":"10956_CR227","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100199","volume":"3","author":"D Raval","year":"2023","unstructured":"Raval D, Undavia JN (2023) A comprehensive assessment of convolutional neural networks for skin and oral cancer detection using medical images. Healthc Anal 3:100199","journal-title":"Healthc Anal"},{"issue":"7","key":"10956_CR228","doi-asserted-by":"publisher","first-page":"9331","DOI":"10.1007\/s11042-021-11477-9","volume":"81","author":"S Dey","year":"2022","unstructured":"Dey S, Roychoudhury R, Malakar S, Sarkar R (2022) Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model. Multimed Tools Appl 81(7):9331\u20139349","journal-title":"Multimed Tools Appl"},{"key":"10956_CR229","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2023.102545","volume":"79","author":"T Hanser","year":"2023","unstructured":"Hanser T (2023) Federated learning for molecular discovery. Curr Opin Struct Biol 79:102545","journal-title":"Curr Opin Struct Biol"},{"issue":"13","key":"10956_CR230","doi-asserted-by":"publisher","first-page":"10922","DOI":"10.1109\/JIOT.2021.3051382","volume":"8","author":"L Su","year":"2021","unstructured":"Su L, Lau VK (2021) Hierarchical federated learning for hybrid data partitioning across multitype sensors. IEEE Int Things J 8(13):10922\u201310939","journal-title":"IEEE Int Things J"},{"key":"10956_CR231","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.ins.2022.08.010","volume":"610","author":"KH Lee","year":"2022","unstructured":"Lee KH, Kim MH (2022) Bayesian inductive learning in group recommendations for seen and unseen groups. Inf Sci 610:725\u2013745","journal-title":"Inf Sci"},{"key":"10956_CR232","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109130","volume":"233","author":"S Kamei","year":"2023","unstructured":"Kamei S, Taghipour S (2023) A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life. Reliab Eng Syst Saf 233:109130","journal-title":"Reliab Eng Syst Saf"},{"issue":"4","key":"10956_CR233","doi-asserted-by":"publisher","first-page":"4289","DOI":"10.1109\/TPAMI.2022.3196503","volume":"45","author":"T Sun","year":"2022","unstructured":"Sun T, Li D, Wang B (2022) Decentralized federated averaging. IEEE Trans Pattern Anal Mach Intell 45(4):4289\u20134301","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"10956_CR234","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1109\/JBHI.2019.2942429","volume":"24","author":"Y Gu","year":"2019","unstructured":"Gu Y, Ge Z, Bonnington CP, Zhou J (2019) Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE J Biomed Health Inf 24(5):1379\u20131393","journal-title":"IEEE J Biomed Health Inf"},{"issue":"2","key":"10956_CR235","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/JBHI.2020.3032060","volume":"25","author":"K Stacke","year":"2020","unstructured":"Stacke K, Eilertsen G, Unger J, Lundstr\u00f6m C (2020) Measuring domain shift for deep learning in histopathology. IEEE J Biomed Health Inf 25(2):325\u2013336","journal-title":"IEEE J Biomed Health Inf"},{"key":"10956_CR236","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106539","volume":"214","author":"R Zoetmulder","year":"2022","unstructured":"Zoetmulder R, Gavves E, Caan M, Marquering H (2022) Domain-and task-specific transfer learning for medical segmentation tasks. Comput Methods Programs Biomed 214:106539","journal-title":"Comput Methods Programs Biomed"},{"key":"10956_CR237","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.nbt.2023.04.006","volume":"76","author":"K Fogelberg","year":"2023","unstructured":"Fogelberg K, Chamarthi S, Maron RC, Niebling J, Brinker TJ (2023) Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation. New Biotechnol 76:106\u2013117","journal-title":"New Biotechnol"},{"key":"10956_CR238","doi-asserted-by":"crossref","unstructured":"Vuong TTL, Vu QD, Jahanifar M, Graham S, Kwak JT, Rajpoot N (2022) Impash: A novel domain-shift resistant representation for colorectal cancer tissue classification. In: European conference on computer vision, Springer, pp 543\u2013555","DOI":"10.1007\/978-3-031-25066-8_31"},{"key":"10956_CR239","doi-asserted-by":"crossref","unstructured":"Marathe A, Anirudh R, Jain N, Bhatele A, Thiagarajan J, Kailkhura B, Yeom J-S, Rountree B, Gamblin T (2017) Performance modeling under resource constraints using deep transfer learning. In: Proceedings of the international conference for high performance computing, Networking, Storage and Analysis, pp 1\u201312","DOI":"10.1145\/3126908.3126969"},{"key":"10956_CR240","unstructured":"Whatmough PN, Zhou C, Hansen P, Venkataramanaiah SK, Seo J-s, Mattina M (2019) Fixynn: Efficient hardware for mobile computer vision via transfer learning, arXiv preprint arXiv:1902.11128"},{"issue":"2","key":"10956_CR241","doi-asserted-by":"publisher","first-page":"450","DOI":"10.3390\/s22020450","volume":"22","author":"HG Abreha","year":"2022","unstructured":"Abreha HG, Hayajneh M, Serhani MA (2022) Federated learning in edge computing: a systematic survey. Sensors 22(2):450","journal-title":"Sensors"},{"issue":"1","key":"10956_CR242","first-page":"1","volume":"9","author":"A Imteaj","year":"2021","unstructured":"Imteaj A, Thakker U, Wang S, Li J, Amini MH (2021) A survey on federated learning for resource-constrained IoT devices. IEEE Int Things J 9(1):1\u201324","journal-title":"IEEE Int Things J"},{"key":"10956_CR243","doi-asserted-by":"crossref","unstructured":"Tran NH, Bao W, Zomaya A, Nguyen MN, Hong CS (2019) Federated learning over wireless networks: Optimization model design and analysis. In: IEEE INFOCOM 2019-IEEE conference on computer communications, IEEE, pp 1387\u20131395","DOI":"10.1109\/INFOCOM.2019.8737464"},{"issue":"6","key":"10956_CR244","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","volume":"37","author":"S Wang","year":"2019","unstructured":"Wang S, Tuor T, Salonidis T, Leung KK, Makaya C, He T, Chan K (2019) Adaptive federated learning in resource constrained edge computing systems. IEEE J Sel Areas Commun 37(6):1205\u20131221","journal-title":"IEEE J Sel Areas Commun"},{"issue":"3","key":"10956_CR245","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/TWC.2019.2961673","volume":"19","author":"K Yang","year":"2020","unstructured":"Yang K, Jiang T, Shi Y, Ding Z (2020) Federated learning via over-the-air computation. IEEE Trans Wirel Commun 19(3):2022\u20132035","journal-title":"IEEE Trans Wirel Commun"},{"issue":"7","key":"10956_CR246","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.3390\/cancers13071590","volume":"13","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Al-Amidie M, Al-Asadi A, Humaidi AJ, Al-Shamma O, Fadhel MA, Zhang J, Santamar\u00eda J, Duan Y (2021) Novel transfer learning approach for medical imaging with limited labeled data. Cancers 13(7):1590","journal-title":"Cancers"},{"key":"10956_CR247","doi-asserted-by":"publisher","first-page":"74901","DOI":"10.1109\/ACCESS.2020.2989273","volume":"8","author":"A Abbas","year":"2020","unstructured":"Abbas A, Abdelsamea MM, Gaber MM (2020) Detrac: Transfer learning of class decomposed medical images in convolutional neural networks. IEEE Access 8:74901\u201374913","journal-title":"IEEE Access"},{"issue":"1","key":"10956_CR248","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K et al (2020) The future of digital health with federated learning. NPJ Digit Med 3(1):119","journal-title":"NPJ Digit Med"},{"issue":"1150","key":"10956_CR249","doi-asserted-by":"publisher","first-page":"20220890","DOI":"10.1259\/bjr.20220890","volume":"96","author":"MHu Rehman","year":"2023","unstructured":"Rehman MHu, Hugo Lopez Pinaya W, Nachev P, Teo JT, Ourselin S, Cardoso MJ (2023) Federated learning for medical imaging radiology. Br J Radiol 96(1150):20220890","journal-title":"Br J Radiol"},{"key":"10956_CR250","unstructured":"Salman H, Jain S, Ilyas A, Engstrom L, Wong E, Madry A (2022) When does bias transfer in transfer learning?, arXiv preprint arXiv:2207.02842"},{"key":"10956_CR251","doi-asserted-by":"crossref","unstructured":"Wang A, Russakovsky O (2023) Overwriting pretrained bias with finetuning data. In: Proceedings of the IEEE\/CVF international conference on computer Vision, pp 3957\u20133968","DOI":"10.1109\/ICCV51070.2023.00366"},{"key":"10956_CR252","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2023.103992","volume":"324","author":"Z Lin","year":"2023","unstructured":"Lin Z, Liu D, Pan W, Yang Q, Ming Z (2023) Transfer learning for collaborative recommendation with biased and unbiased data. Artif Intell 324:103992","journal-title":"Artif Intell"},{"key":"10956_CR253","unstructured":"Xuhong L, Grandvalet Y, Davoine F (2018) Explicit inductive bias for transfer learning with convolutional networks. In: International conference on machine learning, PMLR, pp 2825\u20132834"},{"key":"10956_CR254","doi-asserted-by":"crossref","unstructured":"Saunders D, Byrne B (2020) Reducing gender bias in neural machine translation as a domain adaptation problem, arXiv preprint arXiv:2004.04498","DOI":"10.18653\/v1\/2020.acl-main.690"},{"key":"10956_CR255","doi-asserted-by":"crossref","unstructured":"Nadeem M, Bethke A, Reddy S (2020) Stereoset: Measuring stereotypical bias in pretrained language models, arXiv preprint arXiv:2004.09456","DOI":"10.18653\/v1\/2021.acl-long.416"},{"key":"10956_CR256","unstructured":"Li I (2021) Detecting bias in transfer learning approaches for text classification, arXiv preprint arXiv:2102.02114"},{"key":"10956_CR257","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13058-020-1248-3","volume":"22","author":"MI Jaber","year":"2020","unstructured":"Jaber MI, Song B, Taylor C, Vaske CJ, Benz SC, Rabizadeh S, Soon-Shiong P, Szeto CW (2020) A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival. Breast Cancer Res 22:1\u201310","journal-title":"Breast Cancer Res"},{"key":"10956_CR258","doi-asserted-by":"crossref","unstructured":"Nyman J, Denize T, Bakouny Z, Labaki C, Titchen BM, Bi K, Hari SN, Rosenthal J, Mehta N, Jiang B et al (2023) Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states. Cell Rep Med 4(9)","DOI":"10.1016\/j.xcrm.2023.101189"},{"issue":"5","key":"10956_CR259","doi-asserted-by":"publisher","first-page":"3500","DOI":"10.1039\/C6SC03738K","volume":"8","author":"P Inglese","year":"2017","unstructured":"Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RC (2017) Deep learning and 3d-desi imaging reveal the hidden metabolic heterogeneity of cancer. Chem Sci 8(5):3500\u20133511","journal-title":"Chem Sci"},{"issue":"3","key":"10956_CR260","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1109\/COMST.2021.3090430","volume":"23","author":"LU Khan","year":"2021","unstructured":"Khan LU, Saad W, Han Z, Hossain E, Hong CS (2021) Federated learning for internet of things: Recent advances, taxonomy, and open challenges. IEEE Commun Surv Tutor 23(3):1759\u20131799","journal-title":"IEEE Commun Surv Tutor"},{"issue":"1","key":"10956_CR261","doi-asserted-by":"publisher","first-page":"6494","DOI":"10.1038\/s41467-022-34277-7","volume":"13","author":"J Chen","year":"2022","unstructured":"Chen J, Wang X, Ma A, Wang Q-E, Liu B, Li L, Xu D, Ma Q (2022) Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nat Commun 13(1):6494","journal-title":"Nat Commun"},{"issue":"1","key":"10956_CR262","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1038\/s41467-024-44946-4","volume":"15","author":"B Feng","year":"2024","unstructured":"Feng B, Shi J, Huang L, Yang Z, Feng S-T, Li J, Chen Q, Xue H, Chen X, Wan C et al (2024) Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence. Nat Commun 15(1):742","journal-title":"Nat Commun"},{"key":"10956_CR263","unstructured":"Wang G (2019) Interpret federated learning with shapley values, arXiv preprint arXiv:1905.04519"},{"key":"10956_CR264","doi-asserted-by":"crossref","unstructured":"Qin Z, Yang L, Wang Q, Han Y, Hu Q (2023) Reliable and interpretable personalized federated learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 20422\u201320431","DOI":"10.1109\/CVPR52729.2023.01956"},{"key":"10956_CR265","doi-asserted-by":"crossref","unstructured":"Kim D, Lim W, Hong M, Kim H (2019) The structure of deep neural network for interpretable transfer learning. In: 2019 IEEE International conference on big data and smart computing (BigComp), IEEE, pp 1\u20134","DOI":"10.1109\/BIGCOMP.2019.8679150"},{"key":"10956_CR266","first-page":"1","volume":"71","author":"W Mao","year":"2022","unstructured":"Mao W, Liu J, Chen J, Liang X (2022) An interpretable deep transfer learning-based remaining useful life prediction approach for bearings with selective degradation knowledge fusion. IEEE Trans Instrument Meas 71:1\u201316","journal-title":"IEEE Trans Instrument Meas"},{"key":"10956_CR267","unstructured":"Chen S, Ma K, Zheng Y (2019) Med3d: Transfer learning for 3d medical image analysis, arXiv preprint arXiv:1904.00625"},{"key":"10956_CR268","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s10278-019-00267-3","volume":"33","author":"V Gupta","year":"2020","unstructured":"Gupta V, Demirer M, Bigelow M, Little KJ, Candemir S, Prevedello LM, White RD, O\u2019Donnell TP, Wels M, Erdal BS (2020) Performance of a deep neural network algorithm based on a small medical image dataset: incremental impact of 3d-to-2d reformation combined with novel data augmentation, photometric conversion, or transfer learning. J Digit Imag 33:431\u2013438","journal-title":"J Digit Imag"},{"key":"10956_CR269","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"Cheplygina V, de Bruijne M, Pluim JP (2019) Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 54:280\u2013296","journal-title":"Med Image Anal"},{"key":"10956_CR270","first-page":"75","volume":"15","author":"P Wang","year":"2016","unstructured":"Wang P, Xu W, Sun J, Yang C, Wang G, Sa Y, Hu X-H, Feng Y (2016) A new assessment model for tumor heterogeneity analysis with [18] F-FDG pet images. EXCLI J 15:75","journal-title":"EXCLI J"},{"key":"10956_CR271","doi-asserted-by":"crossref","unstructured":"Zhou Z, Sodha V, Rahman Siddiquee MM, Feng R, Tajbakhsh N, Gotway MB, Liang J (2019) Models genesis: Generic autodidactic models for 3d medical image analysis. In: Medical image computing and computer assisted intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part IV 22, Springer, pp 384\u2013393","DOI":"10.1007\/978-3-030-32251-9_42"},{"key":"10956_CR272","doi-asserted-by":"crossref","unstructured":"Kareem A, Liu H, Velisavljevic V (2023) A federated learning framework for pneumonia image detection using distributed data. Healthc Anal 100204","DOI":"10.1016\/j.health.2023.100204"},{"key":"10956_CR273","doi-asserted-by":"crossref","unstructured":"Repetto M, La Torre D (2022) Breast cancer detection and prediction using federated multicriteria machine learning. In: 2022 5th International conference on signal processing and information security (ICSPIS), IEEE, pp 1\u20134","DOI":"10.1109\/ICSPIS57063.2022.10057227"},{"issue":"1","key":"10956_CR274","doi-asserted-by":"publisher","first-page":"7346","DOI":"10.1038\/s41467-022-33407-5","volume":"13","author":"S Pati","year":"2022","unstructured":"Pati S, Baid U, Edwards B, Sheller M, Wang S-H, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C et al (2022) Federated learning enables big data for rare cancer boundary detection. Nat Commun 13(1):7346","journal-title":"Nat Commun"},{"key":"10956_CR275","doi-asserted-by":"crossref","unstructured":"Arthi NT, Mubin KE, Rahman J, Rafi G, Sheja TT, Reza MT, Alam MA (2022) Decentralized federated learning and deep learning leveraging xai-based approach to classify colorectal cancer. In: 2022 IEEE Asia-Pacific conference on computer science and data engineering (CSDE), IEEE, pp 1\u20136","DOI":"10.1109\/CSDE56538.2022.10089344"},{"key":"10956_CR276","doi-asserted-by":"crossref","unstructured":"Bisong E (2019) Google colaboratory. In: Building machine learning and deep learning models on google cloud platform, Apress, Berkeley, CA, pp 59\u201364","DOI":"10.1007\/978-1-4842-4470-8_7"},{"key":"10956_CR277","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104669","volume":"159","author":"SH Kassani","year":"2022","unstructured":"Kassani SH, Kassani PH, Wesolowski MJ, Schneider KA, Deters R (2022) Deep transfer learning based model for colorectal cancer histopathology segmentation: a comparative study of deep pre-trained models. Int J Med Inf 159:104669","journal-title":"Int J Med Inf"},{"key":"10956_CR278","doi-asserted-by":"crossref","unstructured":"Luo R, Bocklitz T (2023) A systematic study of transfer learning for colorectal cancer detection. Inf Med Unlocked 101292","DOI":"10.1016\/j.imu.2023.101292"},{"key":"10956_CR279","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102275","volume":"126","author":"NR Freitas","year":"2022","unstructured":"Freitas NR, Vieira PM, Cordeiro A, Tinoco C, Morais N, Torres J, Anacleto S, Laguna MP, Lima E, Lima CS (2022) Detection of bladder cancer with feature fusion, transfer learning and CapsNets. Artif Intell Med 126:102275","journal-title":"Artif Intell Med"},{"key":"10956_CR280","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s11548-017-1573-x","volume":"12","author":"S Azizi","year":"2017","unstructured":"Azizi S, Mousavi P, Yan P, Tahmasebi A, Kwak JT, Xu S, Turkbey B, Choyke P, Pinto P, Wood B et al (2017) Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection. Int J Comput Assist Radiol Surg 12:1111\u20131121","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10956_CR281","unstructured":"Zhang T, Feng Y, Feng Y, Zhao Y, Lei Y, Ying N, Yan Z, He Y, Zhang G (2022) Shuffle instances-based vision transformer for pancreatic cancer rose image classification, arXiv preprint arXiv:2208.06833"},{"key":"10956_CR282","unstructured":"Zhang T (2022) Mil-si, https:\/\/github.com\/sagizty\/MIL-SI\/tree\/main"},{"key":"10956_CR283","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107984","volume":"170","author":"W Chorney","year":"2024","unstructured":"Chorney W, Wang H (2024) Towards federated transfer learning in electrocardiogram signal analysis. Comput Biol Med 170:107984","journal-title":"Comput Biol Med"},{"key":"10956_CR284","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101449","volume":"45","author":"MM Ahsan","year":"2024","unstructured":"Ahsan MM, Alam TE, Haque MA, Ali MS, Rifat RH, Nafi AAN, Hossain MM, Islam MK (2024) Enhancing monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning. Inf Med Unlocked 45:101449","journal-title":"Inf Med Unlocked"},{"key":"10956_CR285","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105893","volume":"89","author":"A Rehman","year":"2024","unstructured":"Rehman A, Xing H, Feng L, Hussain M, Gulzar N, Khan MA, Hussain A, Saeed D (2024) FedCSCD-GAN: a secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and gan. Biomedical Signal Processing and Control 89:105893","journal-title":"Biomedical Signal Processing and Control"},{"key":"10956_CR286","doi-asserted-by":"crossref","unstructured":"Wang Y, Shi Q, Chang T-H (2023) Why batch normalization damage federated learning on non-iid data? IEEE transactions on neural networks and learning systems","DOI":"10.1109\/ICASSP49357.2023.10095399"},{"key":"10956_CR287","doi-asserted-by":"crossref","unstructured":"Zhang X, Sun W, Chen Y (2023) Tackling the non-iid issue in heterogeneous federated learning by gradient harmonization, arXiv preprint arXiv:2309.06692","DOI":"10.1109\/LSP.2024.3430042"},{"key":"10956_CR288","doi-asserted-by":"crossref","unstructured":"Li Z, Sun Y, Shao J, Mao Y, Wang JH, Zhang J (2024) Feature matching data synthesis for non-iid federated learning. IEEE Trans Mob Comput","DOI":"10.1109\/TMC.2024.3365295"},{"issue":"3","key":"10956_CR289","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1109\/TWC.2021.3108197","volume":"21","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Feng C, Hong W, Jiang J, Jia C, Quek TQ, Peng M (2021) Federated learning with non-IID data in wireless networks. IEEE Trans Wirel Commun 21(3):1927\u20131942","journal-title":"IEEE Trans Wirel Commun"},{"issue":"8","key":"10956_CR290","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","volume":"32","author":"F Sattler","year":"2020","unstructured":"Sattler F, M\u00fcller K-R, Samek W (2020) Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans Neural Netw Learn Syst 32(8):3710\u20133722","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"10956_CR291","doi-asserted-by":"publisher","first-page":"12057","DOI":"10.1038\/s41598-024-62908-0","volume":"14","author":"S Wu","year":"2024","unstructured":"Wu S, Chen J, Nie X, Wang Y, Zhou X, Lu L, Peng W, Nie Y, Menhaj W (2024) Global prototype distillation for heterogeneous federated learning. Sci Rep 14(1):12057","journal-title":"Sci Rep"},{"issue":"8","key":"10956_CR292","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW (2023) Large language models in medicine. Nat Med 29(8):1930\u20131940","journal-title":"Nat Med"},{"key":"10956_CR293","doi-asserted-by":"crossref","unstructured":"Bose A, Bai L (2023) A fully decentralized homomorphic federated learning framework. In: 2023 IEEE 20th international conference on mobile Ad Hoc and smart systems (MASS), IEEE, pp. 178\u2013185","DOI":"10.1109\/MASS58611.2023.00029"},{"key":"10956_CR294","doi-asserted-by":"crossref","unstructured":"Sadot AAIM, Mehjabin MM, Mahafuz A (2023) A novel approach to efficient multilabel text classification: Bert-federated learning fusion. In: 2023 26th international conference on computer and information technology (ICCIT), IEEE, pp 1\u20136","DOI":"10.1109\/ICCIT60459.2023.10441264"},{"key":"10956_CR295","doi-asserted-by":"crossref","unstructured":"Kuang W, Qian B, Li Z, Chen D, Gao D, Pan X, Xie Y, Li Y, Ding B, Zhou J (2023) Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning, arXiv preprint arXiv:2309.00363","DOI":"10.1145\/3637528.3671573"},{"key":"10956_CR296","doi-asserted-by":"crossref","unstructured":"Liu X-Y, Zhu R, Zha D, Gao J, Zhong S, Qiu M (2023) Differentially private low-rank adaptation of large language model using federated learning, arXiv preprint arXiv:2312.17493","DOI":"10.1145\/3682068"},{"key":"10956_CR297","doi-asserted-by":"crossref","unstructured":"Ye R, Wang W, Chai J, Li D, Li Z, Xu Y, Du Y, Wang Y, Chen S (2024) Openfedllm: Training large language models on decentralized private data via federated learning, arXiv preprint arXiv:2402.06954","DOI":"10.1145\/3637528.3671582"},{"key":"10956_CR298","doi-asserted-by":"crossref","unstructured":"Abou El Houda Z, Hafid AS, Khoukhi L, Brik B (2022) When collaborative federated learning meets blockchain to preserve privacy in healthcare. IEEE Trans Netw Sci Eng","DOI":"10.1109\/TNSE.2022.3211192"},{"key":"10956_CR299","doi-asserted-by":"crossref","unstructured":"Li B, Liu Z, Shao L, Qiu B, Bu H, Tian J (2023) Point transformer with federated learning for predicting breast cancer her2 status from hematoxylin and eosin-stained whole slide images, arXiv preprint arXiv:2312.06454","DOI":"10.1609\/aaai.v38i4.28082"},{"key":"10956_CR300","doi-asserted-by":"crossref","unstructured":"Gao W, Wang D, Huang Y (2023) Federated learning-driven collaborative diagnostic system for metastatic breast cancer, medRxiv 2023\u201310","DOI":"10.1101\/2023.10.20.23297323"},{"key":"10956_CR301","doi-asserted-by":"crossref","unstructured":"Almufareh MF, Tariq N, Humayun M, Almas B (2023) A federated learning approach to breast cancer prediction in a collaborative learning framework. In: Healthcare, Vol 11, MDPI, p 3185","DOI":"10.3390\/healthcare11243185"},{"key":"10956_CR302","doi-asserted-by":"publisher","first-page":"8693","DOI":"10.1109\/ACCESS.2022.3141913","volume":"10","author":"BC Tedeschini","year":"2022","unstructured":"Tedeschini BC, Savazzi S, Stoklasa R, Barbieri L, Stathopoulos I, Nicoli M, Serio L (2022) Decentralized federated learning for healthcare networks: a case study on tumor segmentation. IEEE Access 10:8693\u20138708","journal-title":"IEEE Access"},{"issue":"11","key":"10956_CR303","doi-asserted-by":"publisher","first-page":"5596","DOI":"10.1109\/JBHI.2022.3198440","volume":"26","author":"J Wicaksana","year":"2022","unstructured":"Wicaksana J, Yan Z, Yang X, Liu Y, Fan L, Cheng K-T (2022) Customized federated learning for multi-source decentralized medical image classification. IEEE J Biomed Health Inf 26(11):5596\u20135607","journal-title":"IEEE J Biomed Health Inf"},{"key":"10956_CR304","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.neunet.2023.02.004","volume":"161","author":"K Han","year":"2023","unstructured":"Han K, Kim Y, Han D, Lee H, Hong S (2023) Tl-ADA: Transferable loss-based active domain adaptation. Neural Netw 161:670\u2013681","journal-title":"Neural Netw"},{"key":"10956_CR305","unstructured":"Hajiramezanali E, Zamani Dadaneh S, Karbalayghareh A, Zhou M, Qian X (2018) Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. Adv Neural Inf Process Syst 31"},{"issue":"01","key":"10956_CR306","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1055\/s-0040-1702009","volume":"29","author":"A Choudhary","year":"2020","unstructured":"Choudhary A, Tong L, Zhu Y, Wang MD (2020) Advancing medical imaging informatics by deep learning-based domain adaptation. Yearb Med Inf 29(01):129\u2013138","journal-title":"Yearb Med Inf"},{"key":"10956_CR307","unstructured":"You K, Wang X, Long M, Jordan M (2019) Towards accurate model selection in deep unsupervised domain adaptation. In: International conference on machine learning, PMLR, pp 7124\u20137133"},{"key":"10956_CR308","doi-asserted-by":"crossref","unstructured":"Wang K, Chen Y, Zhang Y, Yang X, Hu C (2023) Iterative self-training based domain adaptation for cross-user semg gesture recognition. IEEE Trans Neural Syst Rehabil Eng","DOI":"10.1109\/TNSRE.2023.3293334"},{"issue":"1","key":"10956_CR309","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13040-023-00338-w","volume":"16","author":"ZL Azher","year":"2023","unstructured":"Azher ZL, Suvarna A, Chen J-Q, Zhang Z, Christensen BC, Salas LA, Vaickus LJ, Levy JJ (2023) Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication. BioData Min 16(1):23","journal-title":"BioData Min"},{"issue":"2","key":"10956_CR310","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1109\/TNNLS.2020.3029181","volume":"33","author":"L Zhen","year":"2020","unstructured":"Zhen L, Hu P, Peng X, Goh RSM, Zhou JT (2020) Deep multimodal transfer learning for cross-modal retrieval. IEEE Trans Neural Netw Learn Syst 33(2):798\u2013810","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"10956_CR311","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1002\/mp.13367","volume":"46","author":"Y Yuan","year":"2019","unstructured":"Yuan Y, Qin W, Buyyounouski M, Ibragimov B, Hancock S, Han B, Xing L (2019) Prostate cancer classification with multiparametric MRI transfer learning model. Med phys 46(2):756\u2013765","journal-title":"Med phys"},{"key":"10956_CR312","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106555","volume":"154","author":"S Zhang","year":"2023","unstructured":"Zhang S, Miao Y, Chen J, Zhang X, Han L, Ran D, Huang Z, Pei N, Liu H, An C (2023) Twist-net: a multi-modality transfer learning network with the hybrid bilateral encoder for hypopharyngeal cancer segmentation. Comput Biol Med 154:106555","journal-title":"Comput Biol Med"},{"key":"10956_CR313","doi-asserted-by":"publisher","first-page":"149808","DOI":"10.1109\/ACCESS.2020.3016780","volume":"8","author":"MJ Horry","year":"2020","unstructured":"Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N (2020) Covid-19 detection through transfer learning using multimodal imaging data, IEEE. Access 8:149808\u2013149824","journal-title":"Access"},{"issue":"12","key":"10956_CR314","doi-asserted-by":"publisher","first-page":"8747","DOI":"10.21037\/qims-23-542","volume":"13","author":"RF Khan","year":"2023","unstructured":"Khan RF, Lee B-D, Lee MS (2023) Transformers in medical image segmentation: a narrative review. Quantitative Imag Med Surg 13(12):8747","journal-title":"Quantitative Imag Med Surg"},{"key":"10956_CR315","unstructured":"Latif S, Zaidi A, Cuayahuitl H, Shamshad F, Shoukat M, Qadir J (2023) Transformers in speech processing: A survey, arXiv preprint arXiv:2303.11607"},{"key":"10956_CR316","doi-asserted-by":"crossref","unstructured":"Shaik T, Tao X, Li L, Xie H, Vel\u00e1squez JD (2023) A survey of multimodal information fusion for smart healthcare: mapping the journey from data to wisdom. Inf Fus 102040","DOI":"10.1016\/j.inffus.2023.102040"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10956-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10956-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10956-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T22:57:55Z","timestamp":1738796275000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10956-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":316,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10956"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10956-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"17 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}