{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:41:52Z","timestamp":1758267712631,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439063"},{"type":"electronic","value":"9783031439070"}],"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\/978-3-031-43907-0_22","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"227-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Weakly-Supervised Positional Contrastive Learning: Application to\u00a0Cirrhosis Classification"],"prefix":"10.1007","author":[{"given":"Emma","family":"Sarfati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre","family":"B\u00f4ne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc-Michel","family":"Roh\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Gori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabelle","family":"Bloch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Azizi, S., et al.: Big self-supervised models advance medical image classification. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3458\u20133468 (2021)","DOI":"10.1109\/ICCV48922.2021.00346"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Barbano, C.A., Dufumier, B., Duchesnay, E., Grangetto, M., Gori, P.: Contrastive learning for regression in multi-site brain age prediction. In: IEEE ISBI (2022)","DOI":"10.1109\/ISBI53787.2023.10230733"},{"key":"22_CR3","unstructured":"Barbano, C.A., Dufumier, B., Tartaglione, E., Grangetto, M., Gori, P.: Unbiased Supervised Contrastive Learning. In: ICLR (2023)"},{"key":"22_CR4","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 12546\u201312558. Curran Associates, Inc. (2020)"},{"key":"22_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., et al.: A simple framework for contrastive learning of visual representations. In: 37th International Conference on Machine Learning (ICML) (2020)"},{"key":"22_CR6","unstructured":"Chen, T., Kornblith, S., Swersky, K., et al.: Big self-supervised models are strong semi-supervised learners. In: NeurIPS (2020)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple Siamese representation learning. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745\u201315753 (2020)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"22_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/978-3-030-87196-3_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"B Dufumier","year":"2021","unstructured":"Dufumier, B., et al.: Contrastive learning with continuous proxy meta-data for 3D MRI classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 58\u201368. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_6"},{"key":"22_CR9","unstructured":"Erickson, B.J., Kirk, S., Lee, et al.: Radiology data from the cancer genome atlas colon adenocarcinoma [TCGA-COAD] collection. (2016)"},{"key":"22_CR10","unstructured":"Grill, J.B., Strub, F., Altch\u00e9, F., et al.: Bootstrap your own latent - a new approach to self-supervised learning. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 21271\u201321284. Curran Associates, Inc. (2020)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9726\u20139735 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR13","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., Teterwak, P., Wang, C., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"8","key":"22_CR14","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1007\/s11548-020-02206-y","volume":"15","author":"Q Li","year":"2020","unstructured":"Li, Q., Yu, B., Tian, X., Cui, X., Zhang, R., Guo, Q.: Deep residual nets model for staging liver fibrosis on plain CT images. Int. J. Comput. Assist. Radiol. Surg. 15(8), 1399\u20131406 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02206-y","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"22_CR15","unstructured":"Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (2017)"},{"key":"22_CR16","unstructured":"Mohamadnejad, M., et al.: Histopathological study of chronic hepatitis B: a comparative study of Ishak and METAVIR scoring systems. Int. J. Organ Transp. Med. 1 (2010)"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Riba, E., Mishkin, D., Ponsa, D., Rublee, E., Bradski, G.: Kornia: an open source differentiable computer vision library for PyTorch. In: Winter Conference on Applications of Computer Vision (2020)","DOI":"10.1109\/WACV45572.2020.9093363"},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Sarfati, E., Bone, A., Rohe, M.M., Gori, P., Bloch, I.: Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies. In: IEEE ISBI. Cartagena de Indias, Colombia (Apr 2023)","DOI":"10.1109\/ISBI53787.2023.10230783"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Shiha, G., Zalata, K.: Ishak versus METAVIR: Terminology, convertibility and correlation with laboratory changes in chronic hepatitis C. In: Takahashi, H. (ed.) Liver Biopsy, chap. 10. IntechOpen, Rijeka (2011)","DOI":"10.5772\/20110"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Taleb, A., Kirchler, M., Monti, R., Lippert, C.: Contig: Self-supervised multimodal contrastive learning for medical imaging with genetics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20908\u201320921 (June 2022)","DOI":"10.1109\/CVPR52688.2022.02024"},{"key":"22_CR22","unstructured":"Wang, X., Qi, G.J.: Contrastive learning with stronger augmentations. CoRR abs\/2104.07713 (2021)"},{"key":"22_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen, J., et al.: Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020)","journal-title":"Med. Image Anal."},{"issue":"12","key":"22_CR24","doi-asserted-by":"publisher","first-page":"9620","DOI":"10.1007\/s00330-021-08046-x","volume":"31","author":"Y Yin","year":"2021","unstructured":"Yin, Y., Yakar, D., Dierckx, R.A.J.O., Mouridsen, K.B., Kwee, T.C., de Haas, R.J.: Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model. Eur. Radiol. 31(12), 9620\u20139627 (2021). https:\/\/doi.org\/10.1007\/s00330-021-08046-x","journal-title":"Eur. Radiol."},{"key":"22_CR25","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: Self-supervised learning via redundancy reduction. In: International Conference on Machine Learning (2021)"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Zeng, D., et al.: Positional contrastive learning for volumetric medical image segmentation. In: MICCAI, pp. 221\u2013230. Springer-Verlag, Berlin, Heidelberg (2021)","DOI":"10.1007\/978-3-030-87196-3_21"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, F., Zheng, Y.: Self supervised deep representation learning for fine-grained body part recognition. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 578\u2013582 (2017)","DOI":"10.1109\/ISBI.2017.7950587"},{"key":"22_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/978-3-030-32251-9_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., et al.: Models genesis: generic autodidactic models for 3D medical image analysis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 384\u2013393. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_42"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Zhuang, X., Li, Y., Hu, Y., Ma, K., Yang, Y., Zheng, Y.: Self-supervised feature learning for 3D medical images by playing a Rubik\u2019s cube. In: MICCAI (2019)","DOI":"10.1007\/978-3-030-32251-9_46"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43907-0_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T18:23:28Z","timestamp":1709835808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43907-0_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439063","9783031439070"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43907-0_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This research study was conducted retrospectively using human data collected from various medical centers, whose Ethics Committees granted their approval. Data was de-identified and processed according to all applicable privacy laws and the Declaration of Helsinki.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}