{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:16:15Z","timestamp":1767183375462,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031184604"},{"type":"electronic","value":"9783031184611"}],"license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-18461-1_18","type":"book-chapter","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T07:15:14Z","timestamp":1665558914000},"page":"275-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning and Few-Shot Learning in the Detection of Skin Cancer: An Overview"],"prefix":"10.1007","author":[{"given":"Olusoji","family":"Akinrinade","sequence":"first","affiliation":[]},{"given":"Chunglin","family":"Du","sequence":"additional","affiliation":[]},{"given":"Samuel","family":"Ajila","sequence":"additional","affiliation":[]},{"given":"Toluwase A.","family":"Olowookere","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijerph182413409","volume":"18","author":"K Das","year":"2021","unstructured":"Das, K., et al.: Machine learning and its application in skin cancer. Int. J. Environ. Res. Public Health 18, 1\u201310 (2021)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"18_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.33588","author":"J Ferlay","year":"2021","unstructured":"Ferlay, J., et al.: Cancer statistics for the year 2020: an overview. Int. J. Cancer (2021). https:\/\/doi.org\/10.1002\/ijc.33588","journal-title":"Int. J. Cancer"},{"key":"18_CR3","doi-asserted-by":"publisher","first-page":"147858","DOI":"10.1109\/ACCESS.2020.3014701","volume":"8","author":"R Ashraf","year":"2020","unstructured":"Ashraf, R., et al.: Region-of-interest based transfer learning assisted framework for skin cancer detection. IEEE Access 8, 147858\u2013147871 (2020)","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Elgamal, M.: Automatic skin cancer images classification. Int. J. Adv. Comput. Sci. Appl. 4 (2013)","key":"18_CR4","DOI":"10.14569\/IJACSA.2013.040342"},{"doi-asserted-by":"crossref","unstructured":"Dildar, M., et al.: Skin cancer detection: a review using deep learning techniques. Int. J. Environ. Res. Public Health 18 (2021)","key":"18_CR5","DOI":"10.3390\/ijerph18105479"},{"key":"18_CR6","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1097\/CM9.0000000000000372","volume":"132","author":"CX Li","year":"2019","unstructured":"Li, C.X., et al.: Artificial intelligence in dermatology: past, present, and future. Chin. Med. J. 132, 2017\u20132020 (2019)","journal-title":"Chin. Med. J."},{"doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018 (2018)","key":"18_CR7","DOI":"10.1155\/2018\/7068349"},{"doi-asserted-by":"crossref","unstructured":"Mahajan, K., Sharma, M., Vig, L.: Meta-dermdiagnosis: few-shot skin disease identification using meta-learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 3142\u20133151, June 2020","key":"18_CR8","DOI":"10.1109\/CVPRW50498.2020.00373"},{"unstructured":"Koch, G.: Siamese neural networks for one-shot image recognition (2011)","key":"18_CR9"},{"unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K.: Matching networks for one shot learning","key":"18_CR10"},{"unstructured":"Santoro, A., Botvinick, M., Lillicrap, T., Deepmind, G., Com, C.G.: One-shot learning with memory-augmented neural networks (2016)","key":"18_CR11"},{"unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning","key":"18_CR12"},{"unstructured":"National Center for Biotechnology Information (NCBI) [Internet]. No Title. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information","key":"18_CR13"},{"doi-asserted-by":"crossref","unstructured":"Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M.: A guide to convolutional neural networks for computer vision. Synth. Lect. Comput. Vis. 8, 1\u2013207 (2018)","key":"18_CR14","DOI":"10.2200\/S00822ED1V01Y201712COV015"},{"key":"18_CR15","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.procs.2018.05.069","volume":"132","author":"S Indolia","year":"2018","unstructured":"Indolia, S., Goswami, A.K., Mishra, S.P., Asopa, P.: Conceptual understanding of convolutional neural network-a deep learning approach. Procedia Comput. Sci. 132, 679\u2013688 (2018)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"18_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00882-4","volume":"3","author":"IS Oyetade","year":"2021","unstructured":"Oyetade, I.S., Ayeni, J.O., Ogunde, A.O., Oguntunde, B.O., Olowookere, T.A.: Hybridized deep convolutional neural network and fuzzy support vector machines for breast cancer detection. SN Comput. Sci. 3(1), 1\u201314 (2021). https:\/\/doi.org\/10.1007\/s42979-021-00882-4","journal-title":"SN Comput. Sci."},{"doi-asserted-by":"crossref","unstructured":"Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.: A reinforcement learning algorithm for automated detection of skin lesions. Appl. Sci. 11 (2021)","key":"18_CR17","DOI":"10.3390\/app11209367"},{"key":"18_CR18","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"key":"18_CR19","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.jrras.2022.03.008","volume":"15","author":"S Medhat","year":"2022","unstructured":"Medhat, S., Abdel-Galil, H., Aboutabl, A.E., Saleh, H.: Skin cancer diagnosis using convolutional neural networks for smartphone images: a comparative study. J. Radiat. Res. Appl. Sci. 15, 262\u2013267 (2022)","journal-title":"J. Radiat. Res. Appl. Sci."},{"doi-asserted-by":"crossref","unstructured":"Yu, L., Chen, H., Duo, Q., Qin, J., Heng, P.-A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36, 994\u20131004 (2017)","key":"18_CR20","DOI":"10.1109\/TMI.2016.2642839"},{"unstructured":"Kalouche, S.: Vision-based classification of skin cancer using deep learning. Stanford\u2019s machine learning course (CS 229) (2016)","key":"18_CR21"},{"doi-asserted-by":"crossref","unstructured":"Mahbod, A., Schaefer, G., Wang, C., Ecker, R., Ellinge, I.: Skin lesion classification using hybrid deep neural networks. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 1229\u20131233, May 2019","key":"18_CR22","DOI":"10.1109\/ICASSP.2019.8683352"},{"key":"18_CR23","doi-asserted-by":"publisher","first-page":"7160","DOI":"10.1109\/ACCESS.2019.2962812","volume":"8","author":"AA Adegun","year":"2020","unstructured":"Adegun, A.A., Viriri, S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8, 7160\u20137172 (2020)","journal-title":"IEEE Access"},{"key":"18_CR24","series-title":"Learning and Analytics in Intelligent Systems","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-030-40850-3_8","volume-title":"Machine Learning with Health Care Perspective","author":"H Nahata","year":"2020","unstructured":"Nahata, H., Singh, S.P.: Deep learning solutions for skin cancer detection and diagnosis. In: Jain, V., Chatterjee, J.M. (eds.) Machine Learning with Health Care Perspective. LAIS, vol. 13, pp. 159\u2013182. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-40850-3_8"},{"key":"18_CR25","doi-asserted-by":"publisher","first-page":"99633","DOI":"10.1109\/ACCESS.2020.2997710","volume":"8","author":"L Wei","year":"2020","unstructured":"Wei, L., Ding, K., Hu, H.: Automatic skin cancer detection in dermoscopy images based on ensemble lightweight deep learning network. IEEE Access 8, 99633\u201399647 (2020)","journal-title":"IEEE Access"},{"unstructured":"DeVries, T., Ramachandram, D.: Skin lesion classification using deep multi-scale convolutional neural networks (2017)","key":"18_CR26"},{"key":"18_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1147\/JRD.2017.2708299","volume":"61","author":"NCF Codella","year":"2017","unstructured":"Codella, N.C.F., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61, 1\u201328 (2017)","journal-title":"IBM J. Res. Dev."},{"key":"18_CR28","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","volume":"29","author":"HA Haenssle","year":"2018","unstructured":"Haenssle, H.A., et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29, 1836\u20131842 (2018)","journal-title":"Ann. Oncol."},{"key":"18_CR29","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.annonc.2019.10.013","volume":"31","author":"HA Haenssle","year":"2020","unstructured":"Haenssle, H.A., et al.: Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann. Oncol. 31, 137\u2013143 (2020)","journal-title":"Ann. Oncol."},{"key":"18_CR30","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","volume":"113","author":"TJ Brinker","year":"2019","unstructured":"Brinker, T.J., et al.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur. J. Cancer 113, 47\u201354 (2019)","journal-title":"Eur. J. Cancer"},{"key":"18_CR31","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1001\/jamadermatol.2018.4378","volume":"155","author":"P Tschandl","year":"2019","unstructured":"Tschandl, P., et al.: Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks. JAMA Dermatol. 155, 58\u201365 (2019)","journal-title":"JAMA Dermatol."},{"key":"18_CR32","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ejca.2019.06.013","volume":"119","author":"RC Maron","year":"2019","unstructured":"Maron, R.C., et al.: Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur. J. Cancer 119, 57\u201365 (2019)","journal-title":"Eur. J. Cancer"},{"unstructured":"Garcia, S.I.: Meta-learning for skin cancer detection using deep learning techniques, pp. 1\u20137 (2021)","key":"18_CR33"},{"doi-asserted-by":"publisher","unstructured":"Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding data augmentation for classification: when to warp? In: 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 (2016). https:\/\/doi.org\/10.1109\/DICTA.2016.7797091","key":"18_CR34","DOI":"10.1109\/DICTA.2016.7797091"},{"doi-asserted-by":"publisher","unstructured":"Miko\u0142ajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop, IIPhDW 2018, pp. 117\u2013122 (2018). https:\/\/doi.org\/10.1109\/IIPHDW.2018.8388338","key":"18_CR35","DOI":"10.1109\/IIPHDW.2018.8388338"},{"doi-asserted-by":"publisher","unstructured":"Kumar, V., Glaude, H., de Lichy, C., Campbell, W.: A closer look at feature space data augmentation for few-shot intent classification. In: DeepLo@EMNLP-IJCNLP 2019 - Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing, pp. 1\u201310 (2021). https:\/\/doi.org\/10.18653\/v1\/d19-6101","key":"18_CR36","DOI":"10.18653\/v1\/d19-6101"},{"doi-asserted-by":"crossref","unstructured":"Duan, R., et al.: A survey of few-shot learning: an effective method for intrusion detection. Secur. Commun. Netw. 2021 (2021)","key":"18_CR37","DOI":"10.1155\/2021\/4259629"},{"unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4078\u20134088, December 2017","key":"18_CR38"},{"key":"18_CR39","first-page":"1","volume":"106","author":"V Prabhu","year":"2019","unstructured":"Prabhu, V., et al.: Few-shot learning for dermatological disease diagnosis. Proc. Mach. Learn. Res. 106, 1\u201315 (2019)","journal-title":"Proc. Mach. Learn. Res."},{"doi-asserted-by":"publisher","unstructured":"Liu, X.J., Li, K., Luan, H., Wang, W., Chen, Z.: Few-shot learning for skin lesion image classification. Multimedia Tools Appl. (2022). https:\/\/doi.org\/10.1007\/s11042-021-11472-0","key":"18_CR40","DOI":"10.1007\/s11042-021-11472-0"},{"doi-asserted-by":"crossref","unstructured":"Xiao, J., Xu, H., Zhao, W., Cheng, C., Gao, H.: A prior-mask-guided few-shot learning for skin lesion segmentation. Computing (2021)","key":"18_CR41","DOI":"10.1007\/s00607-021-00907-z"},{"doi-asserted-by":"crossref","unstructured":"Xiao, J., Xu, H., Fang, D., Cheng, C., Gao, H.: Boosting and rectifying few-shot learning prototype network for skin lesion classification based on the internet of medical things. Wirel. Netw., 0123456789 (2021)","key":"18_CR42","DOI":"10.1007\/s11276-021-02713-z"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18461-1_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T07:22:16Z","timestamp":1665559336000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18461-1_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"ISBN":["9783031184604","9783031184611"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18461-1_18","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022,10,13]]},"assertion":[{"value":"13 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FTC 2022","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of the Future Technologies Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ftc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/FTC","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}