{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:02:25Z","timestamp":1764997345985,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597214"},{"type":"electronic","value":"9783030597221"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59722-1_51","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T17:03:01Z","timestamp":1601658181000},"page":"529-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging"],"prefix":"10.1007","author":[{"given":"Ashwin","family":"Raju","sequence":"first","affiliation":[]},{"given":"Jiawen","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Mohammad MinHazul","family":"Haq","sequence":"additional","affiliation":[]},{"given":"Jitendra","family":"Jonnagaddala","sequence":"additional","affiliation":[]},{"given":"Junzhou","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"51_CR1","unstructured":"https:\/\/www.cancer.net\/cancer-types\/colorectal-cancer\/statistics (2019)"},{"key":"51_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Anatomy-aware Siamese network: exploiting semantic asymmetry for accurate pelvic fracture detection in x-ray images (2020)","DOI":"10.1007\/978-3-030-58592-1_15"},{"issue":"12","key":"51_CR3","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.3390\/cancers11122007","volume":"11","author":"P Gupta","year":"2019","unstructured":"Gupta, P., et al.: Prediction of colon cancer stages and survival period with machine learning approach. Cancers 11(12), 2007 (2019)","journal-title":"Cancers"},{"key":"51_CR4","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":"51_CR5","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. arXiv preprint arXiv:1802.04712 (2018)"},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Jonnagaddala, J., et al.: Integration and analysis of heterogeneous colorectal cancer data for translational research, p. 387 (2016)","DOI":"10.3233\/978-1-61499-658-3-387"},{"issue":"1","key":"51_CR7","doi-asserted-by":"publisher","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","volume":"16","author":"JN Kather","year":"2019","unstructured":"Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019)","journal-title":"PLoS Med."},{"key":"51_CR8","unstructured":"Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. arXiv preprint arXiv:1904.08082 (2019)"},{"key":"51_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-00934-2_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"R Li","year":"2018","unstructured":"Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174\u2013182. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_20"},{"key":"51_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/978-3-030-32239-7_59","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2019","author":"W Li","year":"2019","unstructured":"Li, W., Nguyen, V.D., Liao, H., Wilder, M., Cheng, K., Luo, J.: Patch transformer for multi-tagging whole slide histopathology images. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2019. LNCS, vol. 11764, pp. 532\u2013540. Springer, Cham (2019)"},{"issue":"1","key":"51_CR11","doi-asserted-by":"publisher","first-page":"75","DOI":"10.15388\/Informatica.2018.158","volume":"29","author":"M Mork\u016bnas","year":"2018","unstructured":"Mork\u016bnas, M., Treigys, P., Bernatavi\u010dien\u0117, J., Laurinavi\u010dius, A., Korvel, G.: Machine learning based classification of colorectal cancer tumour tissue in whole-slide images. Informatica 29(1), 75\u201390 (2018)","journal-title":"Informatica"},{"key":"51_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1007\/978-3-030-32239-7_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Muhammad","year":"2019","unstructured":"Muhammad, H., et al.: Unsupervised subtyping of cholangiocarcinoma using a deep clustering convolutional autoencoder. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 604\u2013612. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_67"},{"key":"51_CR13","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3389\/fbioe.2019.00052","volume":"7","author":"CM Shapcott","year":"2019","unstructured":"Shapcott, C.M., Rajpoot, N., Hewitt, K.: Deep learning with sampling for colon cancer histology images. Front. Bioeng. Biotechnol. 7, 52 (2019)","journal-title":"Front. Bioeng. Biotechnol."},{"key":"51_CR14","unstructured":"Tellez, D., van der Laak, J., Ciompi, F.: Gigapixel whole-slide image classification using unsupervised image compression and contrastive training (2018)"},{"key":"51_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/978-3-030-32226-7_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Wang","year":"2019","unstructured":"Wang, Y., et al.: Weakly Supervised Universal Fracture Detection in Pelvic X-Rays. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 459\u2013467. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_51"},{"key":"51_CR16","doi-asserted-by":"publisher","DOI":"10.4225\/53\/5559205bea135","author":"RL Ward","year":"2015","unstructured":"Ward, R.L., Hawkins, N.J.: Molecular and cellular oncology (MCO) study tumour collection. UNSW Aust. (2015). https:\/\/doi.org\/10.4225\/53\/5559205bea135","journal-title":"UNSW Aust."},{"issue":"03","key":"51_CR17","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1055\/s-2007-984859","volume":"20","author":"JS Wu","year":"2007","unstructured":"Wu, J.S.: Rectal cancer staging. Clin. Colon Rectal Surg. 20(03), 148\u2013157 (2007)","journal-title":"Clin. Colon Rectal Surg."},{"key":"51_CR18","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"51_CR19","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019)"},{"key":"51_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-030-23937-4_12","volume-title":"Digital Pathology","author":"J Xu","year":"2019","unstructured":"Xu, J., et al.: Multi-tissue partitioning for whole slide images of colorectal cancer histopathology images with deeptissue net. In: Reyes-Aldasoro, C.C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds.) ECDP 2019. LNCS, vol. 11435, pp. 100\u2013108. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-23937-4_12"},{"key":"51_CR21","doi-asserted-by":"crossref","unstructured":"Yan, C., Yao, J., Li, R., Xu, Z., Huang, J.: Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 103\u2013110 (2018)","DOI":"10.1145\/3233547.3233573"},{"key":"51_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-030-32254-0_36","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yao","year":"2019","unstructured":"Yao, J., Cai, J., Yang, D., Xu, D., Huang, J.: Integrating 3D geometry of organ for improving medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 318\u2013326. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_36"},{"key":"51_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-319-46723-8_75","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"J Yao","year":"2016","unstructured":"Yao, J., Wang, S., Zhu, X., Huang, J.: Imaging biomarker discovery for lung cancer survival prediction. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 649\u2013657. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_75"},{"key":"51_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1007\/978-3-030-32239-7_55","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yao","year":"2019","unstructured":"Yao, J., Zhu, X., Huang, J.: Deep multi-instance learning for survival prediction from whole slide images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 496\u2013504. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_55"},{"key":"51_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/978-3-319-66185-8_46","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013MICCAI 2017","author":"Jiawen Yao","year":"2017","unstructured":"Yao, Jiawen., Zhu, Xinliang., Zhu, Feiyun, Huang, Junzhou: Deep correlational learning for survival prediction from multi-modality data. In: Descoteaux, M., et al. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 406\u2013414. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_46"},{"key":"51_CR26","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, X., Yuen, P.C., Chang, S.F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6210\u20136219 (2019)","DOI":"10.1109\/CVPR.2019.00637"},{"key":"51_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xue, J., Dana, K.: Deep TEN: texture encoding network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 708\u2013717 (2017)","DOI":"10.1109\/CVPR.2017.309"},{"key":"51_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Graham, S., Koohbanani, N.A., Shaban, M., Heng, P.A., Rajpoot,N.: CGC-net: cell graph convolutional network for grading of colorectal cancer histology images. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"51_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., Huang, J.: WSISA: making survival prediction from whole slide Histopathological images. In: CVPR., pp. 7234\u20137242 (2017)","DOI":"10.1109\/CVPR.2017.725"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59722-1_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:07:24Z","timestamp":1759356444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59722-1_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597214","9783030597221"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59722-1_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/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":"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":"1809","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":"542","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":"30% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}