{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T12:45:34Z","timestamp":1749905134171,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031168758"},{"type":"electronic","value":"9783031168765"}],"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\/978-3-031-16876-5_8","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:03:00Z","timestamp":1663196580000},"page":"75-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leverage Supervised and\u00a0Self-supervised Pretrain Models for\u00a0Pathological Survival Analysis via\u00a0a\u00a0Simple and\u00a0Low-cost Joint Representation Tuning"],"prefix":"10.1007","author":[{"given":"Quan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Can","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Ruining","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Zuhayr","family":"Asad","sequence":"additional","affiliation":[]},{"given":"Tianyuan","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Zheyu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yuankai","family":"Huo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Azizi, S., et al.: Big self-supervised models advance medical image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3478\u20133488 (2021)","key":"8_CR1","DOI":"10.1109\/ICCV48922.2021.00346"},{"unstructured":"Bao, H., Dong, L., Wei, F.: Beit: bert pre-training of image transformers. arXiv preprint arXiv:2106.08254 (2021)","key":"8_CR2"},{"doi-asserted-by":"crossref","unstructured":"Bar, Y., Diamant, I., Wolf, L., Greenspan, H.: Deep learning with non-medical training used for chest pathology identification. In: Medical Imaging 2015: Computer-Aided Diagnosis, vol. 9414, p. 94140V. International Society for Optics and Photonics (2015)","key":"8_CR3","DOI":"10.1117\/12.2083124"},{"unstructured":"Bardes, A., Ponce, J., LeCun, Y.: Vicreg: Variance-invariance-covariance regularization for self-supervised learning. arXiv preprint. arXiv:2105.04906 (2021)","key":"8_CR4"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41, 757\u2013770 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR6","first-page":"100198","volume":"7","author":"O Ciga","year":"2021","unstructured":"Ciga, O., Xu, T., Martel, A.L.: Self supervised contrastive learning for digital histopathology. Mach. Learn. Appl. 7, 100198 (2021)","journal-title":"Mach. Learn. Appl."},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.3389\/fphar.2019.01303","volume":"10","author":"L David","year":"2019","unstructured":"David, L., et al.: Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research. Front. Pharmacol. 10, 1303 (2019)","journal-title":"Front. Pharmacol."},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, pp. 770\u2013778 (2016)","key":"8_CR8","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"8_CR9","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1016\/j.kint.2021.01.015","volume":"99","author":"Y Huo","year":"2021","unstructured":"Huo, Y., Deng, R., Liu, Q., Fogo, A.B., Yang, H.: AI applications in renal pathology. Kidney Int. 99(6), 1309\u20131320 (2021)","journal-title":"Kidney Int."},{"issue":"2","key":"8_CR10","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1109\/JBHI.2019.2929264","volume":"24","author":"D Jarrett","year":"2019","unstructured":"Jarrett, D., Yoon, J., van der Schaar, M.: Dynamic prediction in clinical survival analysis using temporal convolutional networks. IEEE J. Biomed. Health Inform. 24(2), 424\u2013436 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"8_CR11","doi-asserted-by":"publisher","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","volume":"16","author":"JN Kather","year":"2019","unstructured":"Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019)","journal-title":"PLoS Med."},{"doi-asserted-by":"crossref","unstructured":"Kieffer, B., Babaie, M., Kalra, S., Tizhoosh, H.R.: Convolutional neural networks for histopathology image classification: training vs. using pre-trained networks. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1\u20136. IEEE (2017)","key":"8_CR12","DOI":"10.1109\/IPTA.2017.8310149"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"101854","DOI":"10.1016\/j.media.2020.101854","volume":"67","author":"YJ Kim","year":"2021","unstructured":"Kim, Y.J., et al.: PAIP 2019: liver cancer segmentation challenge. Med. Image Anal. 67, 101854 (2021)","journal-title":"Med. Image Anal."},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)","key":"8_CR14"},{"key":"8_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-00934-2_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"R Li","year":"2018","unstructured":"Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174\u2013182. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_20"},{"key":"8_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-030-87196-3_10","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Q Liu","year":"2021","unstructured":"Liu, Q., et al.: SimTriplet: simple triplet representation learning with a single GPU. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 102\u2013112. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_10"},{"doi-asserted-by":"crossref","unstructured":"Lu, Y., Jha, A., Huo, Y.: Contrastive learning meets transfer learning: a case study in medical image analysis. arXiv preprint. arXiv:2103.03166 (2021)","key":"8_CR17","DOI":"10.1117\/12.2610990"},{"issue":"13","key":"8_CR18","doi-asserted-by":"publisher","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","volume":"115","author":"P Mobadersany","year":"2018","unstructured":"Mobadersany, P., et al.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci. 115(13), E2970\u2013E2979 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"2","key":"8_CR19","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/JBHI.2020.2992878","volume":"25","author":"R Mormont","year":"2020","unstructured":"Mormont, R., Geurts, P., Mar\u00e9e, R.: Multi-task pre-training of deep neural networks for digital pathology. IEEE J. Biomed. Health Inform. 25(2), 412\u2013421 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"8_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24876-0","volume":"8","author":"M Peikari","year":"2018","unstructured":"Peikari, M., Salama, S., Nofech-Mozes, S., Martel, A.L.: A cluster-then-label semi-supervised learning approach for pathology image classification. Sci. Rep. 8(1), 1\u201313 (2018)","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Rai, T., et al.: Can imagenet feature maps be applied to small histopathological datasets for the classification of breast cancer metastatic tissue in whole slide images?. In: Medical Imaging 2019: Digital Pathology, vol. 10956, pp. 191\u2013200. SPIE (2019)","key":"8_CR21","DOI":"10.1117\/12.2512853"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)","key":"8_CR22"},{"key":"8_CR23","doi-asserted-by":"publisher","first-page":"26022","DOI":"10.1109\/ACCESS.2019.2901049","volume":"7","author":"B Tang","year":"2019","unstructured":"Tang, B., Li, A., Li, B., Wang, M.: Capsurv: capsule network for survival analysis with whole slide pathological images. IEEE Access 7, 26022\u201326030 (2019)","journal-title":"IEEE Access"},{"unstructured":"Tellez, D., van der Laak, J., Ciompi, F.: Gigapixel whole-slide image classification using unsupervised image compression and contrastive training (2018)","key":"8_CR24"},{"doi-asserted-by":"crossref","unstructured":"Thongprayoon, C., et al.: Promises of big data and artificial intelligence in nephrology and transplantation (2020)","key":"8_CR25","DOI":"10.3390\/jcm9041107"},{"issue":"1A","key":"8_CR26","first-page":"A68","volume":"19","author":"K Tomczak","year":"2015","unstructured":"Tomczak, K., Czerwi\u0144ska, P., Wiznerowicz, M.: The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19(1A), A68 (2015)","journal-title":"Contemp. Oncol."},{"issue":"10","key":"8_CR27","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/sim.4154","volume":"30","author":"H Uno","year":"2011","unstructured":"Uno, H., Cai, T., Pencina, M.J., D\u2019Agostino, R.B., Wei, L.J.: On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30(10), 1105\u20131117 (2011)","journal-title":"Stat. Med."},{"key":"8_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-87237-3_18","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Wang","year":"2021","unstructured":"Wang, X., et al.: TransPath: transformer-based self-supervised learning for histopathological image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 186\u2013195. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_18"},{"key":"8_CR29","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":"8_CR30","doi-asserted-by":"publisher","first-page":"101789","DOI":"10.1016\/j.media.2020.101789","volume":"65","author":"J Yao","year":"2020","unstructured":"Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., Huang, J.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med. Image Anal. 65, 101789 (2020)","journal-title":"Med. Image Anal."},{"doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Huang, J.: Deep convolutional neural network for survival analysis with pathological images. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 544\u2013547. IEEE (2016)","key":"8_CR31","DOI":"10.1109\/BIBM.2016.7822579"}],"container-title":["Lecture Notes in Computer Science","Resource-Efficient Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16876-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:18:48Z","timestamp":1663197528000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16876-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031168758","9783031168765"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16876-5_8","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":"REMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Resource-Efficient Medical 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":"22 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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"remia2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-remia.github.io\/","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":"19","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":"13","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":"68% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}