{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:51:55Z","timestamp":1742975515480,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031169601"},{"type":"electronic","value":"9783031169618"}],"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\/978-3-031-16961-8_13","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T06:03:10Z","timestamp":1663308190000},"page":"126-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sequential Multi-task Learning for Histopathology-Based Prediction of Genetic Mutations with Extremely Imbalanced Labels"],"prefix":"10.1007","author":[{"given":"Haleh","family":"Akrami","sequence":"first","affiliation":[]},{"given":"Tosha","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Vajdi","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Brown","sequence":"additional","affiliation":[]},{"given":"Radha","family":"Krishnan","sequence":"additional","affiliation":[]},{"given":"Razvan","family":"Cristescu","sequence":"additional","affiliation":[]},{"given":"Antong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"13_CR1","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning, pp. 233\u2013242. PMLR (2017)"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Ciga, O., Xu, T., Martel, A.L.: Self supervised contrastive learning for digital histopathology. Mach. Learn. Appl. 7, 100198 (2022)","DOI":"10.1016\/j.mlwa.2021.100198"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Coudray, N., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018)","DOI":"10.1038\/s41591-018-0177-5"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Douillard, A., Chen, Y., Dapogny, A., Cord, M.: PLOP: learning without forgetting for continual semantic segmentation. arXiv preprint arXiv:2011.11390 (2020)","DOI":"10.1109\/CVPR46437.2021.00403"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Fu, Y., et al.: Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1(8), 800\u2013810 (2020)","DOI":"10.1038\/s43018-020-0085-8"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Graham, S., Vu, Q.D., Jahanifar, M., Minhas, F., Snead, D., Rajpoot, N.: One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification. arXiv preprint arXiv:2203.00077 (2022)","DOI":"10.1016\/j.media.2022.102685"},{"key":"13_CR7","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21271\u201321284 (2020)"},{"key":"13_CR8","unstructured":"Jung, H., Ju, J., Jung, M., Kim, J.: Less-forgetting learning in deep neural networks. arXiv preprint arXiv:1607.00122 (2016)"},{"key":"13_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":"13_CR10","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, J.M., Akata, Z., Lee, J.: Large loss matters in weakly supervised multi-label classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14156\u201314165 (2022)","DOI":"10.1109\/CVPR52688.2022.01376"},{"key":"13_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104253","volume":"131","author":"J Li","year":"2021","unstructured":"Li, J., et al.: A multi-resolution model for histopathology image classification and localization with multiple instance learning. Comput. Biol. Med. 131, 104253 (2021)","journal-title":"Comput. Biol. Med."},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"13_CR13","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. arXiv preprint arXiv:1706.08840 (2017)"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: revisiting the nearest class mean classifier in online class-incremental continual learning. arXiv preprint arXiv:2103.13885 (2021)","DOI":"10.1109\/CVPRW53098.2021.00398"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54\u201371 (2019)","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Parisi, G.I., Tani, J., Weber, C., Wermter, S.: Lifelong learning of human actions with deep neural network self-organization. Neural Netw. 96, 137\u2013149 (2017)","DOI":"10.1016\/j.neunet.2017.09.001"},{"key":"13_CR17","unstructured":"Rusu, A.A., et al.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)"},{"key":"13_CR18","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"13_CR19","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. arXiv preprint arXiv:1705.08690 (2017)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Soltoggio, A.: Short-term plasticity as cause-effect hypothesis testing in distal reward learning. Biol. Cybern. 109(1), 75\u201394 (2015)","DOI":"10.1007\/s00422-014-0628-0"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Wulczyn, E., et al.: Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS ONE 15(6) (2020)","DOI":"10.1371\/journal.pone.0233678"}],"container-title":["Lecture Notes in Computer Science","Medical Optical Imaging and Virtual Microscopy Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16961-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T08:50:52Z","timestamp":1676796652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16961-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031169601","9783031169618"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16961-8_13","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":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MOVI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis","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":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"movi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/movi2022","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":"25","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":"18","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":"72% - 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":"2.5","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":"3","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)"}}]}}