{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T17:15:31Z","timestamp":1772471731471,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439865","type":"print"},{"value":"9783031439872","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\/978-3-031-43987-2_51","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"528-538","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forensic Histopathological Recognition via\u00a0a\u00a0Context-Aware MIL Network Powered by\u00a0Self-supervised Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Chen","family":"Shen","sequence":"first","affiliation":[]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xinggong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Zeyi","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Kehan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhenyuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chunfeng","family":"Lian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"51_CR1","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: VICReg: Variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906 (2021)"},{"key":"51_CR2","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"51_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An Empirical Study of Training Self-Supervised Vision Transformers. arXiv e-prints (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"issue":"1","key":"51_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1111\/j.1556-4029.2009.01240.x","volume":"55","author":"GL De La Grandmaison","year":"2010","unstructured":"De La Grandmaison, G.L., Charlier, P., Durigon, M.: Usefulness of systematic histological examination in routine forensic autopsy. J. Forensic Sci. 55(1), 85\u201388 (2010)","journal-title":"J. Forensic Sci."},{"key":"51_CR5","volume-title":"Forensic Pathology","author":"D DiMaio","year":"2001","unstructured":"DiMaio, D., DiMaio, V.J.: Forensic Pathology. CRC Press, Boca Raton (2001)"},{"key":"51_CR6","volume-title":"Forensic Pathology: Principles and Practice","author":"D Dolinak","year":"2005","unstructured":"Dolinak, D., Matshes, E., Lew, E.O.: Forensic Pathology: Principles and Practice. Elsevier, Amsterdam (2005)"},{"key":"51_CR7","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"51_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum Contrast for Unsupervised Visual Representation Learning. arXiv e-prints (2019)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"51_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Huang, L., You, S., Zheng, M., Wang, F., Qian, C., Yamasaki, T.: Learning where to learn in cross-view self-supervised learning. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01405"},{"key":"51_CR12","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Attention-based Deep Multiple Instance Learning. arXiv e-prints (2018)"},{"key":"51_CR13","doi-asserted-by":"publisher","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","volume":"30","author":"PT Jiang","year":"2021","unstructured":"Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: LayerCAM: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875\u20135888 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"51_CR14","doi-asserted-by":"crossref","unstructured":"Lee, Y., et al.: Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning. Nat. Biomed. Eng. (2022)","DOI":"10.1038\/s41551-022-00923-0"},{"key":"51_CR15","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"51_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-87237-3_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Li","year":"2021","unstructured":"Li, H., et al.: DT-MIL: deformable transformer for\u00a0multi-instance learning on\u00a0histopathological image. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 206\u2013216. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_20"},{"key":"51_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv e-prints (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"6","key":"51_CR18","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., Williamson, D.F.K., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: 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":"51_CR19","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"51_CR20","doi-asserted-by":"crossref","unstructured":"Stergiou, A., Poppe, R., Kalliatakis, G.: Refining activation downsampling with softpool. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10357\u201310366 (2021)","DOI":"10.1109\/ICCV48922.2021.01019"},{"issue":"21","key":"51_CR21","doi-asserted-by":"publisher","first-page":"13489","DOI":"10.3390\/ijms232113489","volume":"23","author":"G Wang","year":"2022","unstructured":"Wang, G., et al.: An emerging strategy for muscle evanescent trauma discrimination by spectroscopy and chemometrics. Int. J. Mol. Sci. 23(21), 13489 (2022)","journal-title":"Int. J. Mol. Sci."},{"key":"51_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102645","volume":"83","author":"X Wang","year":"2023","unstructured":"Wang, X., et al.: RetCCL: clustering-guided contrastive learning for whole-slide image retrieval. Med. Image Anal. 83, 102645 (2023)","journal-title":"Med. Image Anal."},{"key":"51_CR23","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":"51_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022)","journal-title":"Med. Image Anal."},{"key":"51_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2022.121286","volume":"278","author":"H Wu","year":"2022","unstructured":"Wu, H., et al.: Pathological and ATR-FTIR spectral changes of delayed splenic rupture and medical significance. Spectrochim. Acta. A Mol. Biomol. Spectrosc. 278, 121286 (2022)","journal-title":"Spectrochim. Acta. A Mol. Biomol. Spectrosc."},{"key":"51_CR26","unstructured":"Xie, Z., et al.: Self-Supervised Learning with Swin Transformers. arXiv preprint arXiv:2105.04553 (2021)"},{"key":"51_CR27","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: Self-supervised learning via redundancy reduction. arXiv preprint arXiv:2103.03230 (2021)"},{"key":"51_CR28","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: Deformable Transformers for End-to-End Object Detection. arXiv e-prints (2020)"}],"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\/978-3-031-43987-2_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:33:03Z","timestamp":1710171183000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_51","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":"1 October 2023","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":"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)"}}]}}