{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:59:22Z","timestamp":1767085162793,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164330"},{"type":"electronic","value":"9783031164347"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16434-7_37","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T15:03:08Z","timestamp":1663254188000},"page":"377-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Local Attention Graph-Based Transformer for\u00a0Multi-target Genetic Alteration Prediction"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Reisenb\u00fcchler","sequence":"first","affiliation":[]},{"given":"Sophia J.","family":"Wagner","sequence":"additional","affiliation":[]},{"given":"Melanie","family":"Boxberg","sequence":"additional","affiliation":[]},{"given":"Tingying","family":"Peng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization (2016). https:\/\/doi.org\/10.48550\/ARXIV.1607.06450","DOI":"10.48550\/ARXIV.1607.06450"},{"issue":"5","key":"37_CR2","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1002\/path.5028","volume":"244","author":"LA Cooper","year":"2018","unstructured":"Cooper, L.A., Demicco, E.G., Saltz, J.H., Powell, R.T., Rao, A., Lazar, A.J.: PanCancer insights from the cancer genome atlas: the pathologist\u2019s perspective. J. Pathol. 244(5), 512\u2013524 (2018)","journal-title":"J. Pathol."},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Coudray, N., et al.: Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018)","DOI":"10.1038\/s41591-018-0177-5"},{"key":"37_CR4","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":"37_CR5","unstructured":"Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. In: Methods and Applications, AAAI Workshop on Deep Learning on Graphs (2021)"},{"key":"37_CR6","doi-asserted-by":"publisher","unstructured":"Fu, Y., et al.: Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1(8), 800\u2013810 (2020). https:\/\/doi.org\/10.1038\/s43018-020-0085-8","DOI":"10.1038\/s43018-020-0085-8"},{"key":"37_CR7","unstructured":"Huang, G., Liu, Z., Pleiss, G., Van Der Maaten, L., Weinberger, K.: Convolutional networks with dense connectivity. IEEE Trans. Pattern Anal. Mach. Intell. (2019)"},{"key":"37_CR8","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2127\u20132136. PMLR (2018)"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Kather, J.N., et al.: Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1(8), 789\u2013799 (2020)","DOI":"10.1038\/s43018-020-0087-6"},{"key":"37_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)","DOI":"10.1038\/s41591-019-0462-y"},{"key":"37_CR11","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-030-87237-3_20"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"37_CR13","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Luchini, C., et al.: ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1\/PD-l1 expression and tumour mutational burden: a systematic review-based approach. Ann. Oncol. 30(8), 1232\u20131243 (2019)","DOI":"10.1093\/annonc\/mdz116"},{"key":"37_CR15","doi-asserted-by":"publisher","unstructured":"Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412\u20131421. Association for Computational Linguistics, Lisbon (2015). https:\/\/doi.org\/10.18653\/v1\/D15-1166","DOI":"10.18653\/v1\/D15-1166"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Murchan, P., et al.: Deep learning of histopathological features for the prediction of tumour molecular genetics. Diagnostics 11(8), 1406 (2021)","DOI":"10.3390\/diagnostics11081406"},{"key":"37_CR17","doi-asserted-by":"publisher","unstructured":"Myronenko, A., Xu, Z., Yang, D., Roth, H.R., Xu, D.: Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 329\u2013338. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_32","DOI":"10.1007\/978-3-030-87237-3_32"},{"issue":"1","key":"37_CR18","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979). https:\/\/doi.org\/10.1109\/TSMC.1979.4310076","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Riasatian, A., et al.: Fine-tuning and training of densenet for histopathology image representation using TCGA diagnostic slides. Med. Image Anal. 70, 102032 (2021)","DOI":"10.1016\/j.media.2021.102032"},{"key":"37_CR20","unstructured":"Shao, Z., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification (2021)"},{"key":"37_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"37_CR22","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.patrec.2020.04.008","volume":"135","author":"L Wang","year":"2020","unstructured":"Wang, L., Jiao, Y., Qiao, Y., Zeng, N., Yu, R.: A novel approach combined transfer learning and deep learning to predict TMB from histology image. Pattern Recogn. Lett. 135, 244\u2013248 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"37_CR23","unstructured":"Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks (2020)"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113\u20131120 (2013)","DOI":"10.1038\/ng.2764"},{"key":"37_CR25","unstructured":"Zhang, M.R., Lucas, J., Hinton, G., Ba, J.: Lookahead optimizer: k steps forward, 1 step back (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16434-7_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:46:52Z","timestamp":1710330412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16434-7_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164330","9783031164347"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16434-7_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}