{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:43:51Z","timestamp":1743111831554,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819608393"},{"type":"electronic","value":"9789819608409"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0840-9_10","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:29:36Z","timestamp":1734024576000},"page":"140-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Extended Few-Shot Learning-Based Approach for\u00a0Histopathological Image Classification of\u00a0Pan-Cancer in\u00a0the\u00a0Digestive System"],"prefix":"10.1007","author":[{"given":"Rui","family":"Li","sequence":"first","affiliation":[]},{"given":"Md Mamunur","family":"Rahaman","sequence":"additional","affiliation":[]},{"given":"Xiaoyan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hongzan","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Minghe","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Marcin","family":"Grzegozek","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"issue":"4\u20135","key":"10_CR1","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4\u20135), 198\u2013211 (2007)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"7793","key":"10_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41586-020-1969-6","volume":"578","author":"T Underwood","year":"2020","unstructured":"Underwood, T.: Pan-cancer analysis of whole genomes. Nature 578(7793), 82\u201393 (2020)","journal-title":"Nature"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Sung, H., et al. \"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,\" in CA: a cancer journal for clinicians, vol. 71, no. 3, pp. 209-249, 2021","DOI":"10.3322\/caac.21660"},{"issue":"1","key":"10_CR4","first-page":"451","volume":"10","author":"B Chhikara","year":"2023","unstructured":"Chhikara, B., Parang, K.: Global Cancer Statistics 2022: the trends projection analysis. Chemical Biology Letters 10(1), 451\u2013451 (2023)","journal-title":"Chemical Biology Letters"},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"M Gurcan","year":"2009","unstructured":"Gurcan, M., et al.: Histopathological image analysis: A review. IEEE Rev. Biomed. Eng. 2, 147\u2013171 (2009)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"N. Ghaffar Nia, E. Kaplanoglu, A. Nasab. \"Evaluation of artificial intelligence techniques in disease diagnosis and prediction,\" in Discover Artificial Intelligence, vol. 3, no. 1, pp. 5, 2023","DOI":"10.1007\/s44163-023-00049-5"},{"issue":"3","key":"10_CR7","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/radiol.11091710","volume":"261","author":"B Van Ginneken","year":"2011","unstructured":"Van Ginneken, B., Schaefer-Prokop, C., Prokop, M.: Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261(3), 719\u2013732 (2011)","journal-title":"Radiology"},{"issue":"3","key":"10_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., et al.: Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur) 53(3), 1\u201334 (2020)","journal-title":"ACM computing surveys (csur)"},{"key":"10_CR9","unstructured":"Qin, Z., et al. \"Medical image understanding with pretrained vision language models: A comprehensive study,\" in arXiv preprint arXiv:2209.15517, 2022"},{"key":"10_CR10","unstructured":"S. Chen, K. Ma, Y. Zheng. \"Med3d: Transfer learning for 3d medical image analysis,\" in arXiv preprint arXiv:1904.00625, 2019"},{"issue":"2","key":"10_CR11","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/s43018-020-00169-2","volume":"2","author":"J Ma","year":"2021","unstructured":"Ma, J., et al.: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nature Cancer 2(2), 233\u2013244 (2021)","journal-title":"Nature Cancer"},{"issue":"10","key":"10_CR12","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1109\/TMI.2021.3060551","volume":"40","author":"R Feng","year":"2021","unstructured":"Feng, R., et al.: Interactive few-shot learning: Limited supervision, better medical image segmentation. IEEE Trans. Med. Imaging 40(10), 2575\u20132588 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR13","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"12","key":"10_CR14","doi-asserted-by":"publisher","first-page":"2143","DOI":"10.1109\/TPAMI.2009.151","volume":"31","author":"B Kulis","year":"2009","unstructured":"Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2143\u20132157 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"14","key":"10_CR15","first-page":"850","volume":"II","author":"L Bertinetto","year":"2016","unstructured":"Bertinetto, L., et al.: Fully-convolutional siamese networks for object tracking, in Computer Vision-ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8\u201310 and 15\u201316, 2016. Proceedings, Part II(14), 850\u2013865 (2016)","journal-title":"Proceedings, Part"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Sung, F., et al, \"Learning to compare: Relation network for few-shot learning,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 1199-1208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"10_CR17","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, T., Wang, D.: A survey of transfer learning. Journal of Big data 3, 1\u201340 (2016)","journal-title":"Journal of Big data"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"T. Dietterich, R. Michalski. \"A comparative review of selected methods for learning from examples,\" in Machine learning, pp. 41-81, 1983","DOI":"10.1016\/B978-0-08-051054-5.50007-8"},{"key":"10_CR19","unstructured":"K. Simonyan, A. Zisserman. \"Very deep convolutional networks for large-scale image recognition,\" in arXiv preprint arXiv:1409.1556, 2014"},{"issue":"6","key":"10_CR20","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"4397","DOI":"10.1109\/BigData59044.2023.10386856","volume":"2023","author":"M Gao","year":"2023","unstructured":"Gao, M., et al.: Predicting PD-L1 status of esophageal cancer from H &E images based on FusedNet model, in. IEEE International Conference on Big Data (BigData) 2023, 4397\u20134405 (2023)","journal-title":"IEEE International Conference on Big Data (BigData)"},{"issue":"4","key":"10_CR22","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1016\/j.bbe.2020.09.008","volume":"40","author":"C Sun","year":"2020","unstructured":"Sun, C., et al.: Gastric histopathology image segmentation using a hierarchical conditional random field. Biocybernetics and Biomedical Engineering 40(4), 1535\u20131555 (2020)","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Hu, W., et al. \"EBHI: A new Enteroscope Biopsy Histopathological H &E Image Dataset for image classification evaluation,\" in Physica Medica, vol. 107, pp. 102534, 2023","DOI":"10.1016\/j.ejmp.2023.102534"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Yacouby, R., & Axman, D. (2020). Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In Proceedings of the first workshop on evaluation and comparison of NLP systems (pp. 79-91)","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al, \"Rethinking the inception architecture for computer vision,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826","DOI":"10.1109\/CVPR.2016.308"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"He, K., et al, \"Deep residual learning for image recognition,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR27","unstructured":"Dosovitskiy, A., et al. \"An image is worth 16x16 words: Transformers for image recognition at scale,\" in arXiv preprint arXiv:2010.11929, 2020"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Touvron, H., et al, \"Training data-efficient image transformers & distillation through attention,\" in International conference on machine learning, 2021, pp. 10347-10357","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Bromley, J., et al. \"Signature verification using a\" siamese\" time delay neural network,\" in Advances in neural information processing systems, vol. 6, 1993","DOI":"10.1142\/9789812797926_0003"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Yang, X., et al. \"CAISHI: A Benchmark Histopathological H &E Image Dataset for Cervical Adenocarcinoma in Situ Identification, Retrieval and Few-shot Learning Evaluation,\" in Data in Brief, vol. 53, 2024","DOI":"10.1016\/j.dib.2024.110141"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Li, C., et al. \"A Cervical Histopathology Image Clustering Approach Using Graph based Features,\" in SN Computer Science, vol. 2, 2021","DOI":"10.1007\/s42979-021-00469-z"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al. \"A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks,\" in IEEE Access, vol. 8, 2020, pp. 90931\u201390956","DOI":"10.1109\/ACCESS.2020.2993788"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al. \"Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology,\" in Intelligent Medicine, vol. 3, 2023, pp. 115\u2013128","DOI":"10.1016\/j.imed.2022.05.004"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Chen, H., et al. \"What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review,\" in Artificial Intelligence Review, 2024, Online first","DOI":"10.1007\/s10462-024-10701-w"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Jiang, H., et al. \"Deep Learning for Liver Cancer Histopathology Image Analysis: A Comprehensive Survey,\" in Engineering Applications of Artificial Intelligence, 2024, Online first","DOI":"10.1016\/j.engappai.2024.108436"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0840-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:08:12Z","timestamp":1734026892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0840-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608393","9789819608409"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0840-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}