{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T21:29:00Z","timestamp":1758403740331,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872366"},{"type":"electronic","value":"9783030872373"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87237-3_65","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"680-689","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels"],"prefix":"10.1007","author":[{"given":"Golara","family":"Javadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samareh","family":"Samadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharareh","family":"Bayat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samira","family":"Sojoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Hurtado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvia","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Black","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parvin","family":"Mousavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Purang","family":"Abolmaesumi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"10071","key":"65_CR1","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","volume":"389","author":"HU Ahmed","year":"2017","unstructured":"Ahmed, H.U., et al.: Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer PROMIS: a paired validating confirmatory study. Lancet 389(10071), 815\u2013822 (2017)","journal-title":"Lancet"},{"issue":"10","key":"65_CR2","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.5858\/arpa.2014-0219-SA","volume":"138","author":"MB Amin","year":"2014","unstructured":"Amin, M.B., et al.: The critical role of the pathologist in determining eligibility for active surveillance as a management option in patients with prostate cancer. Arch. Pathol. Lab. Med. 138(10), 1387\u20131405 (2014)","journal-title":"Arch. Pathol. Lab. Med."},{"key":"65_CR3","doi-asserted-by":"crossref","unstructured":"Feng, L., An, B.: Partial label learning with self-guided retraining. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3542\u20133549 (2019)","DOI":"10.1609\/aaai.v33i01.33013542"},{"key":"65_CR4","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Lan, A.: Do we really need gold samples for sample weighting under label noise? In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3922\u20133931, January 2021","DOI":"10.1109\/WACV48630.2021.00397"},{"key":"65_CR5","doi-asserted-by":"crossref","unstructured":"Gong, C., Zhang, H., Yang, J., Tao, D.: Learning with inadequate and incorrect supervision. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 889\u2013894. IEEE (2017)","DOI":"10.1109\/ICDM.2017.110"},{"key":"65_CR6","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems 2018, pp. 8527\u20138537, 4 December 2018"},{"key":"65_CR7","doi-asserted-by":"crossref","unstructured":"Javadi, G., et al.: Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study. Int. J. Comput. Assist. Radiol. Surg. (2020)","DOI":"10.1007\/s11548-020-02168-1"},{"key":"65_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/978-3-030-59716-0_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"G Javadi","year":"2020","unstructured":"Javadi, G., et al.: Complex cancer detector: complex neural networks on non-stationary time series for guiding systematic prostate biopsy. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 524\u2013533. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_50"},{"key":"65_CR9","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: International Conference on Machine Learning, pp. 2304\u20132313. PMLR (2018)"},{"key":"65_CR10","doi-asserted-by":"publisher","first-page":"101759","DOI":"10.1016\/j.media.2020.101759","volume":"65","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)","journal-title":"Med. Image Anal."},{"issue":"1","key":"65_CR11","first-page":"E11","volume":"15","author":"L Klotz","year":"2021","unstructured":"Klotz, L., et al.: Comparison of micro-ultrasound and multiparametric magnetic resonance imaging for prostate cancer: a multicenter, prospective analysis. Can. Urol. Assoc. J. 15(1), E11 (2021)","journal-title":"Can. Urol. Assoc. J."},{"key":"65_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"65_CR13","unstructured":"Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: International Conference on Machine Learning, pp. 4334\u20134343. PMLR (2018)"},{"issue":"7","key":"65_CR14","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1016\/j.ultrasmedbio.2018.02.014","volume":"44","author":"D Rohrbach","year":"2018","unstructured":"Rohrbach, D., Wodlinger, B., Wen, J., Mamou, J., Feleppa, E.: High-frequency quantitative ultrasound for imaging prostate cancer using a novel micro-ultrasound scanner. Ultrasound Med. Biol. 44(7), 1341\u20131354 (2018)","journal-title":"Ultrasound Med. Biol."},{"key":"65_CR15","doi-asserted-by":"crossref","unstructured":"Sedghi, A., et al.: Improving detection of prostate cancer foci via information fusion of MRI and temporal enhanced ultrasound. Int. J. Comput. Assist. Radiol. Surg. (2020)","DOI":"10.1007\/s11548-020-02172-5"},{"key":"65_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s11548-019-01950-0","volume":"14","author":"A Sedghi","year":"2019","unstructured":"Sedghi, A., et al.: Deep neural maps for unsupervised visualization of high-grade cancer in prostate biopsies. Int. J. Comput. Assist. Radiol. Surg. 14, 1009\u20131016 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"10","key":"65_CR17","doi-asserted-by":"publisher","first-page":"3148","DOI":"10.1109\/TMI.2020.2988198","volume":"39","author":"Y Shao","year":"2020","unstructured":"Shao, Y., Wang, J., Wodlinger, B., Salcudean, S.E.: Improving prostate cancer (PCa) classification performance by using three-player minimax game to reduce data source heterogeneity. IEEE Trans. Med. Imaging 39(10), 3148\u20133158 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"65_CR18","unstructured":"Shu, J., Zhao, Q., Chen, K., Xu, Z., Meng, D.: Learning adaptive loss for robust learning with noisy labels. arXiv preprint arXiv:2002.06482 (2020)"},{"key":"65_CR19","unstructured":"Shu, Q.X., Lixuan, Y., Qian, Z., Sanping, Z., Zongben, X., Deyu, M.: Meta-weight-net: learning an explicit mapping for sample weighting. In: NeurIPS, pp. 1919\u20131930 (2019)"},{"key":"65_CR20","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2017)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87237-3_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:16:43Z","timestamp":1632381403000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87237-3_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872366","9783030872373"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87237-3_65","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.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":"1622","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":"531","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":"33% - 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.","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)"}}]}}