{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:47:34Z","timestamp":1772905654721,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"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_58","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"602-611","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Multi-scale Prototypical Transformer for Whole Slide Image Classification"],"prefix":"10.1007","author":[{"given":"Saisai","family":"Ding","sequence":"first","affiliation":[]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Juncheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"8","key":"58_CR1","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. Nat. Med. 25(8), 1301\u20131309 (2019)","journal-title":"Nat. Med."},{"issue":"11","key":"58_CR2","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","volume":"16","author":"K Bera","year":"2019","unstructured":"Bera, K., Schalper, K.A., Rimm, D.L., Velcheti, V., Madabhushi, A.: Artificial intelligence in digital pathology\u2014new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16(11), 703\u2013715 (2019)","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"Zarella, M.D., et al.: A practical guide to whole slide imaging: a white paper from the digital pathology association. Arch. Pathol. Lab. Med. 143(2), 222\u2013234 (2019)","DOI":"10.5858\/arpa.2018-0343-RA"},{"key":"58_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101813","volume":"67","author":"CL Srinidhi","year":"2021","unstructured":"Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)","journal-title":"Med. Image Anal."},{"key":"58_CR5","doi-asserted-by":"crossref","unstructured":"Javed, S., et al.: Cellular community detection for tissue phenotyping in colorectal cancer histology images. Med. Image Anal. 63, 101696 (2020)","DOI":"10.1016\/j.media.2020.101696"},{"issue":"11","key":"58_CR6","doi-asserted-by":"publisher","first-page":"3003","DOI":"10.1109\/TMI.2022.3176598","volume":"41","author":"Y Zheng","year":"2022","unstructured":"Zheng, Y., et al.: A graph-transformer for whole slide image classification. IEEE Trans. Med. Imaging 41(11), 3003\u20133015 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"58_CR7","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. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"58_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":"58_CR9","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":"58_CR10","doi-asserted-by":"crossref","unstructured":"Chen, R.J.: 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"},{"key":"58_CR11","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X.: 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":"58_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-87237-3_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Li","year":"2021","unstructured":"Li, H., et al.: DT-MIL: deformable transformer for\u00a0multi-instance learning on\u00a0histopathological image. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 206\u2013216. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_20"},{"key":"58_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-030-87237-3_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Chai, H., Wang, R., Wang, H., Yang, Y., Wu, H.: Integration of patch features through self-supervised learning and transformer for survival analysis on whole slide images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 561\u2013570. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_54"},{"issue":"10","key":"58_CR14","doi-asserted-by":"publisher","first-page":"2777","DOI":"10.1109\/TMI.2022.3171418","volume":"41","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Yu, L., Ding, X., Liao, X., Wang, L.: Lymph node metastasis prediction from whole slide images with transformer-guided multiinstance learning and knowledge transfer. IEEE Trans. Med. Imaging 41(10), 2777\u20132787 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"58_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101789","volume":"65","author":"J Yao","year":"2020","unstructured":"Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., Huang, J.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med. Image Anal. 65, 101789 (2020)","journal-title":"Med. Image Anal."},{"key":"58_CR16","doi-asserted-by":"publisher","unstructured":"Yang, J., et al.: ReMix: a general and efficient framework for multiple instance learning based whole slide image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention. pp. 35\u201345. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_4","DOI":"10.1007\/978-3-031-16434-7_4"},{"key":"58_CR17","doi-asserted-by":"crossref","unstructured":"Hashimoto, N., et al.: Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3852\u20133861 (2020)","DOI":"10.1109\/CVPR42600.2020.00391"},{"key":"58_CR18","doi-asserted-by":"crossref","unstructured":"Hou, W., et al.: H^ 2-MIL: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 933\u2013941 (2022)","DOI":"10.1609\/aaai.v36i1.19976"},{"key":"58_CR19","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"58_CR20","unstructured":"Tolstikhin, I.O., et al.: MLP-Mixer: an all-MLP architecture for vision. In: Advances in Neural Information Processing Systems, vol. 34, pp. 24261\u201324272 (2021)"},{"key":"58_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"58_CR22","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"58_CR23","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)"},{"issue":"22","key":"58_CR24","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"}],"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_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:34:14Z","timestamp":1710171254000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_58","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)"}}]}}