{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:01:52Z","timestamp":1777568512303,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031340475","type":"print"},{"value":"9783031340482","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-34048-2_52","type":"book-chapter","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:03:35Z","timestamp":1686139415000},"page":"679-691","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Human-Machine Interactive Tissue Prototype Learning for\u00a0Label-Efficient Histopathology Image Segmentation"],"prefix":"10.1007","author":[{"given":"Wentao","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangpeng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanbo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"issue":"18","key":"52_CR1","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1093\/bioinformatics\/btz083","volume":"35","author":"M Amgad","year":"2019","unstructured":"Amgad, M., Elfandy, H., et al.: Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics 35(18), 3461\u20133467 (2019)","journal-title":"Bioinformatics"},{"key":"52_CR2","unstructured":"Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: SODA, pp. 1027\u20131035 (2007)"},{"issue":"22","key":"52_CR3","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"key":"52_CR4","doi-asserted-by":"crossref","unstructured":"Chan, L., Hosseini, M.S., Rowsell, C., et al.: HistoSegNet: semantic segmentation of histological tissue type in whole slide images. In: ICCV, pp. 10662\u201310671 (2019)","DOI":"10.1109\/ICCV.2019.01076"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., et al.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: WACV, pp. 839\u2013847 (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"52_CR6","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: VCIP, pp. 1\u20134 (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"52_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597\u20131607 (2020)"},{"key":"52_CR8","unstructured":"Ester, M., Kriegel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226\u2013231 (1996)"},{"key":"52_CR9","unstructured":"Grill, J.B., Strub, F., Altch\u00e9, F., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: NeurIPS, pp. 21271\u201321284 (2020)"},{"key":"52_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"52_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"52_CR12","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: ICML, pp. 2127\u20132136 (2018)"},{"issue":"5","key":"52_CR13","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1109\/TFUZZ.2020.2966182","volume":"29","author":"F Liu","year":"2020","unstructured":"Liu, F., Deng, Y.: Determine the number of unknown targets in open world based on elbow method. IEEE Trans. Fuzzy Syst. 29(5), 986\u2013995 (2020)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"6","key":"52_CR14","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., et al.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"52_CR15","doi-asserted-by":"crossref","unstructured":"Sarfraz, S., Sharma, V., Stiefelhagen, R.: Efficient parameter-free clustering using first neighbor relations. In: CVPR, pp. 8934\u20138943 (2019)","DOI":"10.1109\/CVPR.2019.00914"},{"key":"52_CR16","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"52_CR17","doi-asserted-by":"crossref","unstructured":"Xu, G., Song, Z., Sun, Z., et al.: CAMEL: a weakly supervised learning framework for histopathology image segmentation. In: CVPR, pp. 10682\u201310691 (2019)","DOI":"10.1109\/ICCV.2019.01078"},{"issue":"11","key":"52_CR18","doi-asserted-by":"publisher","first-page":"3062","DOI":"10.1109\/TMI.2022.3176915","volume":"41","author":"Z Xu","year":"2022","unstructured":"Xu, Z., Lu, D., Luo, J., et al.: Anti-interference from noisy labels: mean-teacher-assisted confident learning for medical image segmentation. IEEE Trans. Med. Imaging 41(11), 3062\u20133073 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"52_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-87193-2_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Z Xu","year":"2021","unstructured":"Xu, Z., et al.: Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 3\u201313. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_1"},{"key":"52_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-031-16443-9_21","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2022","author":"Z Xu","year":"2022","unstructured":"Xu, Z., et al.: Denoising for relaxing: unsupervised domain adaptive fundus image segmentation without source data. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 214\u2013224. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_21"},{"key":"52_CR21","doi-asserted-by":"crossref","unstructured":"Yan, J., Chen, H., Li, X., Yao, J.: Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis. Comput. Med. Imaging Graph. 97, 102053 (2022)","DOI":"10.1016\/j.compmedimag.2022.102053"},{"key":"52_CR22","unstructured":"Yang, J., et al.: Towards better understanding and better generalization of low-shot classification in histology images with contrastive learning. In: ICLR (2022)"},{"key":"52_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-030-87196-3_5","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"P Yang","year":"2021","unstructured":"Yang, P., Hong, Z., Yin, X., Zhu, C., Jiang, R.: Self-supervised visual representation learning for histopathological images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 47\u201357. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_5"},{"key":"52_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34048-2_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:09:41Z","timestamp":1686139781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34048-2_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031340475","9783031340482"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34048-2_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Carlos de Bariloche","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Argentina","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":"12 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2023","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"169","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":"63","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":"37% - 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)"}}]}}