{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T07:06:31Z","timestamp":1779951991366,"version":"3.53.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051844","type":"print"},{"value":"9783032051851","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\/978-3-032-05185-1_49","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T23:47:32Z","timestamp":1758325652000},"page":"507-517","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prototype-Based Multiple Instance Learning for\u00a0Gigapixel Whole Slide Image Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8635-9164","authenticated-orcid":false,"given":"Susu","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6607-8092","authenticated-orcid":false,"given":"Dominique","family":"van Midden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1554-1291","authenticated-orcid":false,"given":"Geert","family":"Litjens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3629-4384","authenticated-orcid":false,"given":"Christian F.","family":"Baumgartner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"49_CR1","unstructured":"Adebayo, J., Muelly, M., Abelson, H., Kim, B.: Post hoc explanations may be ineffective for detecting unknown spurious correlation. In: International conference on learning representations (2022)"},{"issue":"22","key":"49_CR2","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., Veta, M., Van Diest, P.J., Van Ginneken, B., Karssemeijer, N., Litjens, G., Van Der Laak, J.A., Hermsen, M., Manson, Q.F., Balkenhol, M., 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"},{"key":"49_CR3","unstructured":"Bricken, T., Templeton, A., Batson, J., Chen, B., Jermyn, A., Conerly, T., Turner, N., Anil, C., Denison, C., Askell, A., et\u00a0al.: Towards monosemanticity: Decomposing language models with dictionary learning. Transformer Circuits Thread 2 (2023)"},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Bulten, W., Kartasalo, K., Chen, P.H.C., Str\u00f6m, P., Pinckaers, H., Nagpal, K., Cai, Y., Steiner, D.F., Boven, H., Vink, R., Kaa, C.H., Laak, J., Amin, M.B., Evans, A.J., Kwast, T., Allan, R., Humphrey, P.A., Gr\u00f6nberg, H., Samaratunga, H., Delahunt, B., Tsuzuki, T., H\u00e4kkinen, T., Egevad, L., Demkin, M., Dane, S., Tan, F., Valkonen, M., Corrado, G.S., Peng, L., Mermel, C.H., Ruusuvuori, P., Litjens, G., Eklund, M.: The PANDA challenge consortium: Artificial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge. Nat. Med. (2022)","DOI":"10.1038\/s41591-021-01620-2"},{"key":"49_CR5","doi-asserted-by":"crossref","unstructured":"Butke, J., Hashimoto, N., Takeuchi, I., Miyoshi, H., Ohshima, K., Sakuma, J.: Mixing histopathology prototypes into robust slide-level representations for cancer subtyping. In: International Workshop on Machine Learning in Medical Imaging. pp. 114\u2013123. Springer (2023)","DOI":"10.1007\/978-3-031-45676-3_12"},{"key":"49_CR6","unstructured":"Cunningham, H., Ewart, A., Riggs, L., Huben, R., Sharkey, L.: Sparse autoencoders find highly interpretable features in language models. arXiv preprint arXiv:2309.08600 (2023)"},{"issue":"11","key":"49_CR7","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., Jacobsen, J.H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F.A.: Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11), 665\u2013673 (2020)","journal-title":"Nature Machine Intelligence"},{"key":"49_CR8","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":"49_CR9","first-page":"20689","volume":"35","author":"SA Javed","year":"2022","unstructured":"Javed, S.A., Juyal, D., Padigela, H., Taylor-Weiner, A., Yu, L., Prakash, A.: Additive MIL: Intrinsically interpretable multiple instance learning for pathology. Adv. Neural. Inf. Process. Syst. 35, 20689\u201320702 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"49_CR10","doi-asserted-by":"crossref","unstructured":"Kapse, S., Pati, P., Das, S., Zhang, J., Chen, C., Vakalopoulou, M., Saltz, J., Samaras, D., Gupta, R.R., Prasanna, P.: SI-MIL: Taming deep MIL for self-interpretability in gigapixel histopathology. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11226\u201311237 (2024)","DOI":"10.1109\/CVPR52733.2024.01067"},{"key":"49_CR11","unstructured":"Koh, P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B., Liang, P.: Concept bottleneck models. In: International conference on machine learning. pp. 5338\u20135348. PMLR (2020)"},{"key":"49_CR12","unstructured":"Le, N.M., Patel, N., Shen, C., Martin, B., Eng, A., Shah, C., Grullon, S., Juyal, D.: Learning biologically relevant features in a pathology foundation model using sparse autoencoders. In: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond (2024), https:\/\/openreview.net\/forum?id=daV16mhUBd"},{"issue":"3","key":"49_CR13","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","volume":"30","author":"MY Lu","year":"2024","unstructured":"Lu, M.Y., Chen, B., Williamson, D.F., Chen, R.J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L.P., Gerber, G., et al.: A visual-language foundation model for computational pathology. Nat. Med. 30(3), 863\u2013874 (2024)","journal-title":"Nat. Med."},{"issue":"6","key":"49_CR14","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 biomedical engineering 5(6), 555\u2013570 (2021)","journal-title":"Nature biomedical engineering"},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Rao, S., Mahajan, S., B\u00f6hle, M., Schiele, B.: Discover-then-name: Task-agnostic concept bottlenecks via automated concept discovery. In: European Conference on Computer Vision. pp. 444\u2013461. Springer (2024)","DOI":"10.1007\/978-3-031-72980-5_26"},{"key":"49_CR16","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":"49_CR17","unstructured":"Shin, S., Jo, Y., Ahn, S., Lee, N.: A closer look at the intervention procedure of concept bottleneck models. In: International Conference on Machine Learning. pp. 31504\u201331520. PMLR (2023)"},{"key":"49_CR18","unstructured":"Steinmann, D., Stammer, W., Friedrich, F., Kersting, K.: Learning to intervene on concept bottlenecks. arXiv preprint arXiv:2308.13453 (2023)"},{"key":"49_CR19","doi-asserted-by":"crossref","unstructured":"Sun, S., Koch, L.M., Baumgartner, C.F.: Right for the wrong reason: Can interpretable ml techniques detect spurious correlations? In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 425\u2013434. Springer (2023)","DOI":"10.1007\/978-3-031-43895-0_40"},{"key":"49_CR20","unstructured":"Sun, S., Tessier, L., Meeuwsen, F., Grisi, C., van Midden, D., Litjens, G., Baumgartner, C.F.: Label-free concept based multiple instance learning for gigapixel histopathology (2025), https:\/\/arxiv.org\/abs\/2501.02922"},{"key":"49_CR21","unstructured":"Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner, C.F.: Attri-net: A globally and locally inherently interpretable model for multi-label classification using class-specific counterfactuals. arXiv preprint arXiv:2406.05477 (2024)"},{"key":"49_CR22","doi-asserted-by":"crossref","unstructured":"Vranic, S., Gatalica, Z.: The role of pathology in the era of personalized (precision) medicine: A brief review. (2021)","DOI":"10.5644\/ama2006-124.325"}],"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\/978-3-032-05185-1_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T23:47:41Z","timestamp":1758325661000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05185-1_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051844","9783032051851"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05185-1_49","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"}}]}}