{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:22:06Z","timestamp":1779380526290,"version":"3.53.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04458-y","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T17:25:26Z","timestamp":1680629126000},"page":"20311-20326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2854-2990","authenticated-orcid":false,"given":"Huafeng","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijie","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2167-1343","authenticated-orcid":false,"given":"Jeyan","family":"Thiyagalingam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frans","family":"Coenen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5360-6493","authenticated-orcid":false,"given":"Jionglong","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"issue":"1-2","key":"4458_CR1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich TG, Lathrop RH, Lozano-P\u00e9rez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1-2):31\u201371","journal-title":"Artif Intell"},{"key":"4458_CR2","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.patcog.2017.10.009","volume":"77","author":"M-A Carbonneau","year":"2018","unstructured":"Carbonneau M-A, Cheplygina V, Granger E, Gagnon G (2018) Multiple instance learning: a survey of problem characteristics and applications. Pattern Recogn 77:329\u2013353","journal-title":"Pattern Recogn"},{"issue":"5","key":"4458_CR3","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.media.2014.04.006","volume":"18","author":"T Tong","year":"2014","unstructured":"Tong T, Wolz R, Gao Q, Guerrero R, Hajnal JV, Rueckert D, Initiative ADN, et al (2014) Multiple instance learning for classification of dementia in brain mri. Medical Image Anal 18(5):808\u2013818","journal-title":"Medical Image Anal"},{"issue":"12","key":"4458_CR4","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1109\/TPAMI.2006.248","volume":"28","author":"Y Chen","year":"2006","unstructured":"Chen Y, Bi J, Wang JZ (2006) Miles: multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28(12):1931\u20131947","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4458_CR5","doi-asserted-by":"publisher","first-page":"264","DOI":"10.3389\/fmed.2019.00264","volume":"6","author":"N Dimitriou","year":"2019","unstructured":"Dimitriou N, Arandjelovi\u0107 O., Caie PD (2019) Deep learning for whole slide image analysis: an overview. Front Med 6:264","journal-title":"Front Med"},{"key":"4458_CR6","doi-asserted-by":"crossref","unstructured":"Xu Y, Mo T, Feng Q, Zhong P, Lai M, Eric I, Chang C (2014) Deep learning of feature representation with multiple instance learning for medical image analysis. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1626\u20131630","DOI":"10.1109\/ICASSP.2014.6853873"},{"key":"4458_CR7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.compbiomed.2018.04.004","volume":"96","author":"M Yousefi","year":"2018","unstructured":"Yousefi M, Krzy\u017cak A, Suen CY (2018) Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Comput Biology Med 96:283\u2013293","journal-title":"Comput Biology Med"},{"key":"4458_CR8","unstructured":"Ilse M, Tomczak J, Welling M (2018) Attention-based deep multiple instance learning. In: International conference on machine learning. PMLR, pp 2127\u20132136"},{"key":"4458_CR9","doi-asserted-by":"publisher","first-page":"101789","DOI":"10.1016\/j.media.2020.101789","volume":"65","author":"J Yao","year":"2020","unstructured":"Yao J, Zhu X, Jonnagaddala J, Hawkins N, Huang J (2020) Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med Image Anal 65:101789","journal-title":"Med Image Anal"},{"key":"4458_CR10","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.inffus.2019.06.024","volume":"53","author":"Q Yao","year":"2020","unstructured":"Yao Q, Wang R, Fan X, Liu J, Li Y (2020) Multi-class arrhythmia detection from 12-lead varied-length ecg using attention-based time-incremental convolutional neural network. Inf Fusion 53:174\u2013182","journal-title":"Inf Fusion"},{"issue":"8","key":"4458_CR11","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TMI.2020.2996256","volume":"39","author":"Z Han","year":"2020","unstructured":"Han Z, Wei B, Hong Y, Li T, Cong J, Zhu X, Wei H, Zhang W (2020) Accurate screening of covid-19 using attention-based deep 3d multiple instance learning. IEEE Trans Med Imaging 39 (8):2584\u20132594","journal-title":"IEEE Trans Med Imaging"},{"key":"4458_CR12","unstructured":"Rymarczyk D, Borowa A, Tabor J, Zieli\u0144ski B (2020) Kernel self-attention in deep multiple instance learning, arXiv:2005.12991"},{"key":"4458_CR13","unstructured":"Maron O, Lozano-P\u00e9rez T (1998) A framework for multiple-instance learning. Adv Neural Inf Process Syst:570\u2013576"},{"key":"4458_CR14","first-page":"425","volume":"19","author":"G Fung","year":"2007","unstructured":"Fung G, Dundar M, Krishnapuram B, Rao RB (2007) Multiple instance learning for computer aided diagnosis. Adv Neural Inf Process Syst 19:425","journal-title":"Adv Neural Inf Process Syst"},{"key":"4458_CR15","unstructured":"Maron O, Ratan AL (1998) Multiple-instance learning for natural scene classification. In: ICML. Citeseer, vol 98, pp 341\u2013349"},{"key":"4458_CR16","doi-asserted-by":"crossref","unstructured":"Wu J, Zhao Y, Zhu J-Y, Luo S, Tu Z (2014) Milcut: a sweeping line multiple instance learning paradigm for interactive image segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 256\u2013263","DOI":"10.1109\/CVPR.2014.40"},{"key":"4458_CR17","doi-asserted-by":"crossref","unstructured":"Yang C, Dong M, Hua J (2006) Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201906). IEEE, vol 2, pp 2057\u20132063","DOI":"10.1109\/CVPR.2006.250"},{"issue":"8","key":"4458_CR18","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","volume":"33","author":"B Babenko","year":"2010","unstructured":"Babenko B, Yang M-H, Belongie S (2010) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619\u20131632","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4458_CR19","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patcog.2015.11.022","volume":"53","author":"Y Yi","year":"2016","unstructured":"Yi Y, Lin M (2016) Human action recognition with graph-based multiple-instance learning. Pattern Recogn 53:148\u2013162","journal-title":"Pattern Recogn"},{"key":"4458_CR20","doi-asserted-by":"crossref","unstructured":"Yun K, Honorio J, Chattopadhyay D, Berg TL, Samaras D (2012) Two-person interaction detection using body-pose features and multiple instance learning. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops. IEEE, pp 28\u201335","DOI":"10.1109\/CVPRW.2012.6239234"},{"key":"4458_CR21","doi-asserted-by":"crossref","unstructured":"Cheplygina V, S\u00f8rensen L, Tax DM, Pedersen JH, Loog M, de Bruijne M (2014) Classification of copd with multiple instance learning. In: 2014 22Nd international conference on pattern recognition. IEEE, pp 1508\u20131513","DOI":"10.1109\/ICPR.2014.268"},{"issue":"11","key":"4458_CR22","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.1109\/TMI.2017.2724070","volume":"36","author":"Z Jia","year":"2017","unstructured":"Jia Z, Huang X, Eric I, Chang C, Xu Y (2017) Constrained deep weak supervision for histopathology image segmentation. IEEE Trans Med Imaging 36(11):2376\u20132388","journal-title":"IEEE Trans Med Imaging"},{"key":"4458_CR23","doi-asserted-by":"crossref","unstructured":"Wu J, Yu Y, Huang C, Yu K (2015) Deep multiple instance learning for image classification and auto-annotation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3460\u20133469","DOI":"10.1109\/CVPR.2015.7298968"},{"issue":"12","key":"4458_CR24","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1093\/bioinformatics\/btw252","volume":"32","author":"OZ Kraus","year":"2016","unstructured":"Kraus OZ, Ba JL, Frey BJ (2016) Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 32(12):52\u201359","journal-title":"Bioinformatics"},{"issue":"4","key":"4458_CR25","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1049\/iet-ipr.2017.0636","volume":"12","author":"L Zhou","year":"2018","unstructured":"Zhou L, Zhao Y, Yang J, Yu Q, Xu X (2018) Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Process 12(4):563\u2013571","journal-title":"IET Image Process"},{"key":"4458_CR26","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805"},{"key":"4458_CR27","unstructured":"Pappas N, Popescu-Belis A (2017) Multilingual hierarchical attention networks for document classification. arXiv:1707.00896"},{"key":"4458_CR28","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 652\u2013660"},{"key":"4458_CR29","doi-asserted-by":"crossref","unstructured":"Le-Khac PH, Healy G, Smeaton AF (2020) Contrastive representation learning: a framework and review. IEEE Access","DOI":"10.1109\/ACCESS.2020.3031549"},{"key":"4458_CR30","unstructured":"Li X, Liu S, De Mello S, Wang X, Kautz J, Yang M-H (2019) Joint-task self-supervised learning for temporal correspondence. arXiv:1909.11895"},{"key":"4458_CR31","doi-asserted-by":"crossref","unstructured":"Wang X, Huang Q, Celikyilmaz A, Gao J, Shen D, Wang Y-F, Wang WY, Zhang L (2019) Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6629\u20136638","DOI":"10.1109\/CVPR.2019.00679"},{"key":"4458_CR32","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, pp 160\u2013167","DOI":"10.1145\/1390156.1390177"},{"key":"4458_CR33","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. arXiv:1803.07728"},{"key":"4458_CR34","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA (2016) Colorful image colorization. In: European conference on computer vision. Springer, pp 649\u2013666","DOI":"10.1007\/978-3-319-46487-9_40"},{"issue":"2","key":"4458_CR35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1148\/radiol.2020200823","volume":"296","author":"HX Bai","year":"2020","unstructured":"Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, Pan I, Shi L-B, Wang D-C, Mei J et al (2020) Performance of radiologists in differentiating covid-19 from non-covid-19 viral pneumonia at chest ct. Radiology 296(2):46\u201354","journal-title":"Radiology"},{"key":"4458_CR36","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"4458_CR37","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning. PMLR, pp 1597\u20131607"},{"key":"4458_CR38","first-page":"12546","volume":"33","author":"K Chaitanya","year":"2020","unstructured":"Chaitanya K, Erdil E, Karani N, Konukoglu E (2020) Contrastive learning of global and local features for medical image segmentation with limited annotations. Adv Neural Inf Process Syst 33:12546\u201312558","journal-title":"Adv Neural Inf Process Syst"},{"key":"4458_CR39","doi-asserted-by":"crossref","unstructured":"Wu Y, Zeng D, Wang Z, Shi Y, Hu J (2021) Federated contrastive learning for volumetric medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 367\u2013377","DOI":"10.1007\/978-3-030-87199-4_35"},{"key":"4458_CR40","doi-asserted-by":"publisher","first-page":"102447","DOI":"10.1016\/j.media.2022.102447","volume":"79","author":"K Wang","year":"2022","unstructured":"Wang K, Zhan B, Zu C, Wu X, Zhou J, Zhou L, Wang Y (2022) Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning. Med Image Anal 79:102447","journal-title":"Med Image Anal"},{"issue":"6","key":"4458_CR41","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/TMI.2018.2884053","volume":"38","author":"Y Wang","year":"2018","unstructured":"Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush DS, Lin W, Wu X, Zhou J (2018) Shen, d.: 3d auto-context-based locality adaptive multi-modality gans for pet synthesis. IEEE Trans Med Imaging 38(6):1328\u20131339","journal-title":"IEEE Trans Med Imaging"},{"key":"4458_CR42","first-page":"77","volume":"102335","author":"Y Luo","year":"2022","unstructured":"Luo Y, Zhou L, Zhan B, Fei Y, Zhou J, Wang Y, Shen D (2022) Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis. Med Image Anal 102335:77","journal-title":"Med Image Anal"},{"key":"4458_CR43","doi-asserted-by":"crossref","unstructured":"Tsai Y-HH, Bai S, Yamada M, Morency L-P, Salakhutdinov R (2019) Transformer dissection: a unified understanding of transformer\u2019s attention via the lens of kernel. arXiv:1908.11775","DOI":"10.18653\/v1\/D19-1443"},{"key":"4458_CR44","unstructured":"Kim H, Mnih A, Schwarz J, Garnelo M, Eslami A, Rosenbaum D, Vinyals O, Teh YW (2019) Attentive neural processes. arXiv:1901.05761"},{"issue":"2","key":"4458_CR45","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"issue":"6","key":"4458_CR46","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Proc Mag 29(6):141\u2013142","journal-title":"IEEE Signal Proc Mag"},{"key":"4458_CR47","doi-asserted-by":"crossref","unstructured":"Gelasca ED, Byun J, Obara B, Manjunath B (2008) Evaluation and benchmark for biological image segmentation. In: 2008 15Th IEEE international conference on image processing. IEEE, pp 1816\u20131819","DOI":"10.1109\/ICIP.2008.4712130"},{"issue":"5","key":"4458_CR48","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196\u20131206","journal-title":"IEEE Trans Med Imaging"},{"key":"4458_CR49","unstructured":"Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2000) The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography. Medical Physics Publishing, pp 212\u2013218"},{"key":"4458_CR50","unstructured":"Hu H, Coenen F, Ma F, Thiyagalingam J, Su J (2018) Location-aware convolutional neural networks based breast tumor detection"},{"key":"4458_CR51","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.08.026","volume":"74","author":"X Wang","year":"2018","unstructured":"Wang X, Yan Y, Tang P, Bai X, Liu W (2018) Revisiting multiple instance neural networks. Pattern Recogn 74:15\u201324","journal-title":"Pattern Recogn"},{"key":"4458_CR52","doi-asserted-by":"crossref","unstructured":"Yi J, Zhou B (2022) Attention awareness multiple instance neural network. arXiv:2205.13750","DOI":"10.1007\/978-3-031-15934-3_48"},{"key":"4458_CR53","doi-asserted-by":"crossref","unstructured":"Yang M, Zhang Y-X, Wang X, Min F (2021) Multi-instance ensemble learning with discriminative bags. IEEE Trans Syst, Man, Cybern: Syst","DOI":"10.1109\/TSMC.2021.3125040"},{"issue":"11","key":"4458_CR54","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P, et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"4458_CR55","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Medical Image Anal 42:60\u201388","journal-title":"Medical Image Anal"},{"issue":"2","key":"4458_CR56","doi-asserted-by":"publisher","first-page":"039","DOI":"10.5565\/rev\/elcvia.216","volume":"8","author":"O Martins","year":"2009","unstructured":"Martins O, Braz Junior G, Corr\u00eaa Silva A., Cardoso de Paiva A, Gattass M, et al (2009) Detection of masses in digital mammograms using k-means and support vector machine. ELCVIA: Electr Lett Comput Vision Image Anal 8(2):039\u201350","journal-title":"ELCVIA: Electr Lett Comput Vision Image Anal"},{"key":"4458_CR57","doi-asserted-by":"crossref","unstructured":"Dhungel N, Carneiro G, Bradley AP (2015) Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 1\u20138","DOI":"10.1109\/DICTA.2015.7371234"},{"issue":"8","key":"4458_CR58","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1118\/1.2214177","volume":"33","author":"R Bellotti","year":"2006","unstructured":"Bellotti R, De Carlo F, Tangaro S, Gargano G, Maggipinto G, Castellano M, Massafra R, Cascio D, Fauci F, Magro R et al (2006) A completely automated cad system for mass detection in a large mammographic database. Medical physics 33(8):3066\u20133075","journal-title":"Medical physics"},{"issue":"10","key":"4458_CR59","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1016\/j.compbiomed.2007.01.009","volume":"37","author":"P Delogu","year":"2007","unstructured":"Delogu P, Fantacci ME, Kasae P, Retico A (2007) Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier. Comput Biol Med 37(10):1479\u20131491","journal-title":"Comput Biol Med"},{"key":"4458_CR60","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.neucom.2013.05.053","volume":"128","author":"Z Wang","year":"2014","unstructured":"Wang Z, Yu G, Kang Y, Zhao Y, Qu Q (2014) Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing 128:175\u2013184","journal-title":"Neurocomputing"},{"issue":"1","key":"4458_CR61","doi-asserted-by":"publisher","first-page":"4165","DOI":"10.1038\/s41598-018-22437-z","volume":"8","author":"D Ribli","year":"2018","unstructured":"Ribli D, Horv\u00e1th A., Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with deep learning. Sci Rep 8(1):4165\u20134171","journal-title":"Sci Rep"},{"key":"4458_CR62","doi-asserted-by":"crossref","unstructured":"Zhang S, Zou B, Xu B, Su J, Hu H (2021) An efficient deep learning framework of covid-19 ct scans using contrastive learning and ensemble strategy. In: 2021 IEEE international conference on progress in informatics and computing (PIC). IEEE, pp 388\u2013396","DOI":"10.1109\/PIC53636.2021.9687080"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04458-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04458-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04458-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T16:08:49Z","timestamp":1744214929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04458-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,4]]},"references-count":62,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4458"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04458-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,4]]},"assertion":[{"value":"5 January 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2023","order":2,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}