{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:25:48Z","timestamp":1742955948688,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031456756"},{"type":"electronic","value":"9783031456763"}],"license":[{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-45676-3_42","type":"book-chapter","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T08:02:16Z","timestamp":1697270536000},"page":"417-426","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cascaded Cross-Attention Networks for\u00a0Data-Efficient Whole-Slide Image Classification Using Transformers"],"prefix":"10.1007","author":[{"given":"Firas","family":"Khader","sequence":"first","affiliation":[]},{"given":"Jakob Nikolas","family":"Kather","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Han","sequence":"additional","affiliation":[]},{"given":"Sven","family":"Nebelung","sequence":"additional","affiliation":[]},{"given":"Christiane","family":"Kuhl","sequence":"additional","affiliation":[]},{"given":"Johannes","family":"Stegmaier","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Truhn","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"42_CR1","doi-asserted-by":"crossref","unstructured":"Abnar, S., Zuidema, W.: Quantifying attention flow in transformers (2020). arXiv:2005.00928","DOI":"10.18653\/v1\/2020.acl-main.385"},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Canny, J.: A Computational approach to edge detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol. 8(6), pp. 679\u2013698 (1986)","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Cui, M., Zhang, D.Y.: Artificial intelligence and computational pathology. Lab. Invest. 101(4), 412\u2013422 (2021). https:\/\/www.nature.com\/articles\/s41374-020-00514-0","DOI":"10.1038\/s41374-020-00514-0"},{"key":"42_CR4","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\,\\times \\, $$16 words: transformers for image recognition at scale (2020). arXiv:2010.11929"},{"key":"42_CR5","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: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3851\u20133860. IEEE, Seattle, WA, USA (2020). https:\/\/ieeexplore.ieee.org\/document\/9157776\/","DOI":"10.1109\/CVPR42600.2020.00391"},{"key":"42_CR6","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90. ISSN: 1063-6919","DOI":"10.1109\/CVPR.2016.90"},{"key":"42_CR7","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: Proceedings of the 35th International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018). https:\/\/proceedings.mlr.press\/v80\/ilse18a.html. ISSN: 2640-3498"},{"key":"42_CR8","doi-asserted-by":"publisher","unstructured":"Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., Carreira, J.: Perceiver: general perception with iterative attention (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.03206, arXiv:2103.03206","DOI":"10.48550\/arXiv.2103.03206"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Kanavati, F., et al.: Weakly-supervised learning for lung carcinoma classification using deep learning. Sci. Rep. 10(1), 9297 (2020). https:\/\/www.nature.com\/articles\/s41598-020-66333-x","DOI":"10.1038\/s41598-020-66333-x"},{"key":"42_CR10","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). https:\/\/www.nature.com\/articles\/s41591-019-0462-y","DOI":"10.1038\/s41591-019-0462-y"},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Konda, R., Wu, H., Wang, M.D.: Graph convolutional neural networks to classify whole slide images. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1334\u20131338 (2020)","DOI":"10.1109\/ICASSP40776.2020.9054598"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Lu, M.Y., Williamson, D.F.K., 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). https:\/\/www.nature.com\/articles\/s41551-020-00682-w","DOI":"10.1038\/s41551-020-00682-w"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Marini, N., et al.: Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. NPJ Digit. Med. 5(1), 1\u201318 (2022). https:\/\/www.nature.com\/articles\/s41746-022-00635-4","DOI":"10.1038\/s41746-022-00635-4"},{"key":"42_CR14","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Saul, N., Gro\u00dfberger, L.: UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018). https:\/\/doi.org\/10.21105\/joss.00861","DOI":"10.21105\/joss.00861"},{"key":"42_CR15","unstructured":"Shao, Z., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In: Advances in Neural Information Processing Systems, vol. 34, pp. 2136\u20132147. Curran Associates, Inc. (2021). https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/10c272d06794d3e5785d5e7c5356e9ff-Abstract.html"},{"key":"42_CR16","unstructured":"Sharma, Y., Shrivastava, A., Ehsan, L., Moskaluk, C.A., Syed, S., Brown, D.: Cluster-to-Conquer: a framework for end-to-end multi-instance learning for whole slide image classification. In: Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, pp. 682\u2013698. PMLR (2021). https:\/\/proceedings.mlr.press\/v143\/sharma21a.html ISSN: 2640-3498"},{"issue":"11","key":"42_CR17","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.14645","volume":"2","author":"N Tomita","year":"2019","unstructured":"Tomita, N., Abdollahi, B., Wei, J., Ren, B., Suriawinata, A., Hassanpour, S.: Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw. Open 2(11), e1914645 (2019)","journal-title":"JAMA Netw. Open"},{"key":"42_CR18","unstructured":"Tu, M., Huang, J., He, X., Zhou, B.: Multiple instance learning with graph neural networks (2019). arXiv:1906.04881"},{"key":"42_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., Yan, Y., Tang, P., Bai, X., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15\u201324 (2018). arXiv:1610.02501","DOI":"10.1016\/j.patcog.2017.08.026"},{"key":"42_CR21","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: RetCCL: clustering-guided contrastive learning for whole-slide image retrieval. Med. Image Anal. 83, 102645 (2023). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841522002730","DOI":"10.1016\/j.media.2022.102645"},{"key":"42_CR22","doi-asserted-by":"crossref","unstructured":"Xiong, Y., et al.: A Nystr\u00f6m-based algorithm for approximating self-attention (2021). arXiv:2102.03902","DOI":"10.1609\/aaai.v35i16.17664"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45676-3_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:55:43Z","timestamp":1710330943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45676-3_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,15]]},"ISBN":["9783031456756","9783031456763"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45676-3_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,10,15]]},"assertion":[{"value":"15 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2023?pli=1","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"139","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":"93","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":"67% - 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":"2","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}