{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:43:28Z","timestamp":1773215008682,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049834","type":"print"},{"value":"9783032049841","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04984-1_38","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:24:47Z","timestamp":1758299087000},"page":"395-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PCR-MIL: Phenotype Clustering Reinforced Multiple Instance Learning for\u00a0Whole Slide Image Classification"],"prefix":"10.1007","author":[{"given":"Jingjiao","family":"Lou","sequence":"first","affiliation":[]},{"given":"Qingtao","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"38_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: dMIL-transformer: multiple instance learning via integrating morphological and spatial information for lymph node metastasis classification. IEEE J. Biomed. Health Inform. 27(9), 4433\u20134443 (2023)","DOI":"10.1109\/JBHI.2023.3285275"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Fei, H., Zhang, Y., Ren, Y., Ji, D.: Optimizing attention for sequence modeling via reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. 33(8), 3612\u20133621 (2021)","DOI":"10.1109\/TNNLS.2021.3053633"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Gadermayr, M., Tschuchnig, M.: Multiple instance learning for digital pathology: a review of the state-of-the-art, limitations & future potential. Comput. Med. Imaging Graph. 102337 (2024)","DOI":"10.1016\/j.compmedimag.2024.102337"},{"key":"38_CR5","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":"38_CR6","doi-asserted-by":"crossref","unstructured":"Kather, J.N., et al.: Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25(7), 1054\u20131056 (2019)","DOI":"10.1038\/s41591-019-0462-y"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Van der Laak, J., Litjens, G., Ciompi, F.: Deep learning in histopathology: the path to the clinic. Nat. Med. 27(5), 775\u2013784 (2021)","DOI":"10.1038\/s41591-021-01343-4"},{"key":"38_CR8","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":"38_CR9","doi-asserted-by":"crossref","unstructured":"Li, H., Xu, H.: Deep reinforcement learning for robust emotional classification in facial expression recognition. Knowl.-Based Syst. 204, 106172 (2020)","DOI":"10.1016\/j.knosys.2020.106172"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Pati, P., et al.: Hierarchical graph representations in digital pathology. Med. Image Anal. 75, 102264 (2022)","DOI":"10.1016\/j.media.2021.102264"},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Schirris, Y., Gavves, E., Nederlof, I., Horlings, H.M., Teuwen, J.: Deepsmile: contrastive self-supervised pre-training benefits MSI and HRD classification directly from H &E whole-slide images in colorectal and breast cancer. Med. Image Anal. 79, 102464 (2022)","DOI":"10.1016\/j.media.2022.102464"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"38_CR13","unstructured":"Shao, Z., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Song, H., Tang, J., Xiao, H., Hu, J.: Rethinking overfitting of multiple instance learning for whole slide image classification. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp. 546\u2013551. IEEE (2023)","DOI":"10.1109\/ICME55011.2023.00100"},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Yu, S., et al.: MIL-VT: multiple instance learning enhanced vision transformer for fundus image classification. In: Medical Image Computing and Computer Assisted Intervention\u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September\u20131 October 2021, Proceedings, Part VIII 24, pp. 45\u201354. Springer (2021)","DOI":"10.1007\/978-3-030-87237-3_5"},{"key":"38_CR16","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 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04984-1_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:24:57Z","timestamp":1758299097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04984-1_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049834","9783032049841"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04984-1_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}