{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:34:30Z","timestamp":1778081670465,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","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\/978-3-031-43993-3_53","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"548-558","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning Large Margin Sparse Embeddings for\u00a0Open Set Medical Diagnosis"],"prefix":"10.1007","author":[{"given":"Mingyuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jicong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"53_CR1","doi-asserted-by":"publisher","first-page":"105474","DOI":"10.1016\/j.dib.2020.105474","volume":"30","author":"A Acevedo","year":"2020","unstructured":"Acevedo, A., Merino, A., Alf\u00e9rez, S., Molina, \u00c1., Bold\u00fa, L., Rodellar, J.: A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief 30, 105474 (2020)","journal-title":"Data Brief"},{"issue":"11","key":"53_CR2","first-page":"8065","volume":"44","author":"G Chen","year":"2021","unstructured":"Chen, G., Peng, P., Wang, X., Tian, Y.: Adversarial reciprocal points learning for open set recognition. IEEE T-PAMI 44(11), 8065\u20138081 (2021)","journal-title":"IEEE T-PAMI"},{"key":"53_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-58580-8_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Chen","year":"2020","unstructured":"Chen, G., et al.: Learning open set network with discriminative reciprocal points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 507\u2013522. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_30"},{"key":"53_CR4","unstructured":"Dhamija, A.R., G\u00fcnther, M., Boult, T.: Reducing network agnostophobia. In: NeurIPS, vol. 31 (2018)"},{"issue":"4","key":"53_CR5","doi-asserted-by":"publisher","first-page":"5985","DOI":"10.1109\/LRA.2020.3010753","volume":"5","author":"D Fontanel","year":"2020","unstructured":"Fontanel, D., Cermelli, F., Mancini, M., Bulo, S.R., Ricci, E., Caputo, B.: Boosting deep open world recognition by clustering. IEEE Robot. Autom. Lett. 5(4), 5985\u20135992 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"53_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-031-16434-7_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","author":"A Galdran","year":"2022","unstructured":"Galdran, A., Hewitt, K.J., Ghaffari Laleh, N., Kather, J.N., Carneiro, G., Gonz\u00e1lez Ballester, M.A.: Test time transform prediction for open set histopathological image recognition. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 263\u2013272. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_26"},{"key":"53_CR7","unstructured":"Geifman, Y., El-Yaniv, R.: Selective classification for deep neural networks. In: NeurIPS, vol. 30 (2017)"},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Geng, C., Huang, S., Chen, S.: Recent advances in open set recognition: a survey. IEEE T-PAMI 43(10), 3614\u20133631 (2020)","DOI":"10.1109\/TPAMI.2020.2981604"},{"key":"53_CR9","unstructured":"Grandvalet, Y., Rakotomamonjy, A., Keshet, J., Canu, S.: Support vector machines with a reject option. In: NeurIPS, vol. 21 (2008)"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Hassen, M., Chan, P.K.: Learning a neural-network-based representation for open set recognition. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 154\u2013162 (2020)","DOI":"10.1137\/1.9781611976236.18"},{"key":"53_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":"53_CR12","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2017)"},{"issue":"5","key":"53_CR13","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018)","journal-title":"Cell"},{"key":"53_CR14","doi-asserted-by":"crossref","unstructured":"Kong, S., Ramanan, D.: OpenGAN: open-set recognition via open data generation. In: ICCV, pp. 813\u2013822 (2021)","DOI":"10.1109\/ICCV48922.2021.00085"},{"key":"53_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/978-3-030-58548-8_26","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Liu","year":"2020","unstructured":"Liu, B., et al.: Negative margin matters: understanding margin in few-shot classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 438\u2013455. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_26"},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: CVPR, pp. 212\u2013220 (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: CVPR, pp. 2537\u20132546 (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"Lu, J., Xu, Y., Li, H., Cheng, Z., Niu, Y.: PMAL: open set recognition via robust prototype mining. In: AAAI, vol. 36, pp. 1872\u20131880 (2022)","DOI":"10.1609\/aaai.v36i2.20081"},{"key":"53_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-031-19806-9_21","volume-title":"Computer Vision - ECCV 2022","author":"W Moon","year":"2022","unstructured":"Moon, W., Park, J., Seong, H.S., Cho, C.H., Heo, J.P.: Difficulty-aware simulator for open set recognition. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13685, pp. 365\u2013381. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19806-9_21"},{"key":"53_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1007\/978-3-030-01231-1_38","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L Neal","year":"2018","unstructured":"Neal, L., Olson, M., Fern, X., Wong, W.-K., Li, F.: Open set learning with counterfactual images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 620\u2013635. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_38"},{"issue":"2","key":"53_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"53_CR22","unstructured":"Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M.H., Sabokrou, M.: A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: solutions and future challenges. arXiv preprint arXiv:2110.14051 (2021)"},{"issue":"7","key":"53_CR23","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2012","unstructured":"Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. IEEE T-PAMI 35(7), 1757\u20131772 (2012)","journal-title":"IEEE T-PAMI"},{"issue":"1","key":"53_CR24","doi-asserted-by":"publisher","first-page":"7146","DOI":"10.1038\/s41598-020-63649-6","volume":"10","author":"Y Shu","year":"2020","unstructured":"Shu, Y., Shi, Y., Wang, Y., Huang, T., Tian, Y.: P-ODN: prototype-based open deep network for open set recognition. Sci. Rep. 10(1), 7146 (2020)","journal-title":"Sci. Rep."},{"key":"53_CR25","unstructured":"Thulasidasan, S., Bhattacharya, T., Bilmes, J., Chennupati, G., Mohd-Yusof, J.: Combating label noise in deep learning using abstention. arXiv preprint arXiv:1905.10964 (2019)"},{"key":"53_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: CVPR, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"issue":"3","key":"53_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"53_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, B., Che, T., Zhou, K., Liu, Z., Li, D.: Energy-based open-world uncertainty modeling for confidence calibration. In: ICCV, pp. 9302\u20139311 (2021)","DOI":"10.1109\/ICCV48922.2021.00917"},{"key":"53_CR29","doi-asserted-by":"crossref","unstructured":"Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning-the good, the bad and the ugly. In: CVPR, pp. 4582\u20134591 (2017)","DOI":"10.1109\/CVPR.2017.328"},{"key":"53_CR30","doi-asserted-by":"crossref","unstructured":"Yang, H.M., Zhang, X.Y., Yin, F., Liu, C.L.: Robust classification with convolutional prototype learning. In: CVPR, pp. 3474\u20133482 (2018)","DOI":"10.1109\/CVPR.2018.00366"},{"issue":"1","key":"53_CR31","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., et al.: MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10(1), 41 (2023)","journal-title":"Sci. Data"},{"issue":"1","key":"53_CR32","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1038\/s41598-023-28589-x","volume":"13","author":"Z Yu","year":"2023","unstructured":"Yu, Z., Shi, Y.: Centralized space learning for open-set computer-aided diagnosis. Sci. Rep. 13(1), 1630 (2023)","journal-title":"Sci. Rep."},{"key":"53_CR33","doi-asserted-by":"crossref","unstructured":"Yue, Z., Wang, T., Sun, Q., Hua, X.S., Zhang, H.: Counterfactual zero-shot and open-set visual recognition. In: CVPR, pp. 15404\u201315414 (2021)","DOI":"10.1109\/CVPR46437.2021.01515"},{"issue":"6","key":"53_CR34","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1109\/JPROC.2020.2989782","volume":"108","author":"XY Zhang","year":"2020","unstructured":"Zhang, X.Y., Liu, C.L., Suen, C.Y.: Towards robust pattern recognition: a review. Proc. IEEE 108(6), 894\u2013922 (2020)","journal-title":"Proc. IEEE"},{"key":"53_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, D.W., Ye, H.J., Zhan, D.C.: Learning placeholders for open-set recognition. In: CVPR, pp. 4401\u20134410 (2021)","DOI":"10.1109\/CVPR46437.2021.00438"}],"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\/978-3-031-43993-3_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:11:55Z","timestamp":1712074315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_53","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)"}}]}}