{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:28:15Z","timestamp":1773232095297,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439865","type":"print"},{"value":"9783031439872","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43987-2_45","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"467-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Iteratively Coupled Multiple Instance Learning from\u00a0Instance to\u00a0Bag Classifier for\u00a0Whole Slide Image Classification"],"prefix":"10.1007","author":[{"given":"Hongyi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Luyang","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ruofeng","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hongjie","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Lanfen","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"22","key":"45_CR1","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"issue":"8","key":"45_CR2","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Med. 25(8), 1301\u20131309 (2019)","journal-title":"Nature Med."},{"issue":"3","key":"45_CR3","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1007\/s12072-022-10323-w","volume":"16","author":"Q Chen","year":"2022","unstructured":"Chen, Q., et al.: Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images. Hepatol. Int. 16(3), 590\u2013602 (2022)","journal-title":"Hepatol. Int."},{"key":"45_CR4","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"issue":"7","key":"45_CR5","doi-asserted-by":"publisher","first-page":"1948","DOI":"10.1053\/j.gastro.2022.02.025","volume":"162","author":"N Cheng","year":"2022","unstructured":"Cheng, N., et al.: Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images. Gastroenterology 162(7), 1948\u20131961 (2022)","journal-title":"Gastroenterology"},{"key":"45_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"45_CR7","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424\u20132433 (2016)","DOI":"10.1109\/CVPR.2016.266"},{"key":"45_CR9","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"45_CR10","unstructured":"Jin, C., Guo, Z., Lin, Y., Luo, L., Chen, H.: Label-efficient deep learning in medical image analysis: challenges and future directions. arXiv preprint arXiv:2303.12484 (2023)"},{"key":"45_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"45_CR12","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"45_CR13","doi-asserted-by":"crossref","unstructured":"Liu, K., et al.: Multiple instance learning via iterative self-paced supervised contrastive learning. arXiv preprint arXiv:2210.09452 (2022)","DOI":"10.1109\/CVPR52729.2023.00327"},{"key":"45_CR14","unstructured":"Lu, M., et al.: Smile: sparse-attention based multiple instance contrastive learning for glioma sub-type classification using pathological images. In: MICCAI Workshop on Computational Pathology, pp. 159\u2013169. PMLR (2021)"},{"issue":"7861","key":"45_CR15","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","volume":"594","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., et al.: AI-based pathology predicts origins for cancers of unknown primary. Nature 594(7861), 106\u2013110 (2021)","journal-title":"Nature"},{"issue":"6","key":"45_CR16","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nature Biomed. Eng."},{"key":"45_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/978-3-030-58526-6_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Luo","year":"2020","unstructured":"Luo, Z., et al.: Weakly-supervised action localization with expectation-maximization multi-instance learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 729\u2013745. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_43"},{"key":"45_CR18","unstructured":"Maron, O., Lozano-P\u00e9rez, T.: A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems, vol. 10 (1997)"},{"issue":"3","key":"45_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"45_CR20","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"45_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102256","volume":"75","author":"CL Srinidhi","year":"2022","unstructured":"Srinidhi, C.L., Kim, S.W., Chen, F.D., Martel, A.L.: Self-supervised driven consistency training for annotation efficient histopathology image analysis. Med. Image Anal. 75, 102256 (2022)","journal-title":"Med. Image Anal."},{"key":"45_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Q., Chechik, G., Sun, C., Shen, B.: Instance-level label propagation with multi-instance learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2943\u20132949 (2017)","DOI":"10.24963\/ijcai.2017\/410"},{"issue":"12","key":"45_CR23","doi-asserted-by":"publisher","first-page":"3431","DOI":"10.1109\/JBHI.2020.2983730","volume":"24","author":"X Wang","year":"2020","unstructured":"Wang, X., et al.: UD-MIL: uncertainty-driven deep multiple instance learning for oct image classification. IEEE J. Biomed. Health Inform. 24(12), 3431\u20133442 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"45_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, Y., Zhang, D., Liu, S., Chen, M., Cai, W.: Whole slide image classification via iterative patch labelling. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1408\u20131412. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451551"},{"key":"45_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: DTFD-MIL: double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18802\u201318812 (2022)","DOI":"10.1109\/CVPR52688.2022.01824"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43987-2_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:32:56Z","timestamp":1710171176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}