{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:54:13Z","timestamp":1783439653779,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819985579","type":"print"},{"value":"9789819985586","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8558-6_26","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T20:02:02Z","timestamp":1703534522000},"page":"309-320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Task-Incremental Medical Image Classification with\u00a0Task-Specific Batch Normalization"],"prefix":"10.1007","author":[{"given":"Xuchen","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizhuo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weishi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","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 in Brief 30 (2020). https:\/\/doi.org\/10.1016\/j.dib.2020.105474","DOI":"10.1016\/j.dib.2020.105474"},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in Brief 28, 104863 (2020). https:\/\/doi.org\/10.1016\/j.dib.2019.104863","DOI":"10.1016\/j.dib.2019.104863"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: ECCV, pp. 139\u2013154 (2018)","DOI":"10.1007\/978-3-030-01219-9_9"},{"issue":"22","key":"26_CR4","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":"26_CR5","unstructured":"Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M.: Lung and colon cancer histopathological image dataset (LC25000). arXiv preprint arXiv:1912.12142 (2019)"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Chaudhry, A., Dokania, P.K., Ajanthan, T., Torr, P.H.: Riemannian walk for incremental learning: understanding forgetting and intransigence. In: ECCV, pp. 532\u2013547 (2018)","DOI":"10.1007\/978-3-030-01252-6_33"},{"key":"26_CR7","unstructured":"Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Ghamsarian, N., et al.: LensID: a CNN-RNN-based framework towards lens irregularity detection in cataract surgery videos. In: MICCAI, pp. 76\u201386 (2021)","DOI":"10.1007\/978-3-030-87237-3_8"},{"key":"26_CR9","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":"26_CR10","doi-asserted-by":"crossref","unstructured":"Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831\u2013839 (2019)","DOI":"10.1109\/CVPR.2019.00092"},{"key":"26_CR11","unstructured":"Institute, N.C.: TCGA dataset (2006). https:\/\/www.cancer.gov\/about-nci\/organization\/ccg\/research\/structural-genomics\/tcga"},{"issue":"1","key":"26_CR12","first-page":"29","volume":"7","author":"A Janowczyk","year":"2016","unstructured":"Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. JPI 7(1), 29 (2016)","journal-title":"JPI"},{"key":"26_CR13","unstructured":"Karras, T., et al.: Alias-free generative adversarial networks. In: NeurIPS (2021)"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"26_CR15","doi-asserted-by":"publisher","unstructured":"Kather, J.N., Halama, N., Marx, A.: 100,000 histological images of human colorectal cancer and healthy tissue (2018). https:\/\/doi.org\/10.5281\/zenodo.1214456","DOI":"10.5281\/zenodo.1214456"},{"key":"26_CR16","doi-asserted-by":"publisher","unstructured":"Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLOS Med. 16(1), 1\u201322 (2019). https:\/\/doi.org\/10.1371\/journal.pmed.1002730","DOI":"10.1371\/journal.pmed.1002730"},{"key":"26_CR17","unstructured":"Ke, Z., Liu, B., Ma, N., Xu, H., Shu, L.: Achieving forgetting prevention and knowledge transfer in continual learning. In: NeurIPS (2021)"},{"key":"26_CR18","unstructured":"Kebede, A.F.: Oral cancer dataset, version 1 (2021). https:\/\/www.kaggle.com\/datasets\/ashenafifasilkebede\/dataset"},{"key":"26_CR19","doi-asserted-by":"publisher","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131.e9 (2018). https:\/\/doi.org\/10.1016\/j.cell.2018.02.010","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"26_CR20","doi-asserted-by":"publisher","unstructured":"Kermany, D.S., Zhang, K., Goldbaum, M.H.: Large dataset of labeled optical coherence tomography (Oct) and chest X-RAY images (2018). https:\/\/doi.org\/10.17632\/rscbjbr9sj.3","DOI":"10.17632\/rscbjbr9sj.3"},{"key":"26_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ICLR (2015)"},{"issue":"13","key":"26_CR22","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. PNAS 114(13), 3521\u20133526 (2017)","journal-title":"PNAS"},{"key":"26_CR23","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. Rep. 4(7) (2009)"},{"issue":"6","key":"26_CR24","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"issue":"12","key":"26_CR25","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. PAMI 40(12), 2935\u20132947 (2017)","journal-title":"PAMI"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhong, C., Wang, R., Zheng, W.S.: Continual learning of new diseases with dual distillation and ensemble strategy. In: MICCAI, pp. 169\u2013178 (2020)","DOI":"10.1007\/978-3-030-59710-8_17"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Schiele, B., Sun, Q.: Adaptive aggregation networks for class-incremental learning. In: CVPR, pp. 2544\u20132553 (2021)","DOI":"10.1109\/CVPR46437.2021.00257"},{"issue":"7","key":"26_CR28","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1038\/nmeth.2083","volume":"9","author":"V Ljosa","year":"2012","unstructured":"Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat. Methods 9(7), 637\u2013637 (2012)","journal-title":"Nat. Methods"},{"key":"26_CR29","unstructured":"PourKeshavarzi, M., Zhao, G., Sabokrou, M.: Looking back on learned experiences for class\/task incremental learning. In: ICLR (2022)"},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: ICARL: incremental classifier and representation learning. In: CVPR, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)","DOI":"10.1109\/ICCV.2015.314"},{"key":"26_CR32","doi-asserted-by":"publisher","unstructured":"Tschandl, P.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions (2018). https:\/\/doi.org\/10.7910\/DVN\/DBW86T","DOI":"10.7910\/DVN\/DBW86T"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology. In: MICCAI, pp. 210\u2013218 (2018)","DOI":"10.1007\/978-3-030-00934-2_24"},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Wei, J., et al.: A petri dish for histopathology image analysis. In: Artificial Intelligence in Medicine, pp. 11\u201324 (2021)","DOI":"10.1007\/978-3-030-77211-6_2"},{"key":"26_CR35","doi-asserted-by":"crossref","unstructured":"Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A.L., Le, Q.V.: Adversarial examples improve image recognition. In: CVPR, pp. 819\u2013828 (2020)","DOI":"10.1109\/CVPR42600.2020.00090"},{"key":"26_CR36","unstructured":"Yang, J., et al.: MedMNIST v2: a large-scale lightweight benchmark for 2D and 3D biomedical image classification. arXiv preprint arXiv:2110.14795 (2021)"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cui, Z., Xu, J., Zhong, C., Wang, R., Zheng, W.S.: Continual learning with Bayesian model based on a fixed pre-trained feature extractor. In: MICCAI, pp. 397\u2013406 (2021)","DOI":"10.1007\/978-3-030-87240-3_38"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8558-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T20:52:48Z","timestamp":1730926368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8558-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819985579","9789819985586"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8558-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"3,78","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":"3,69","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}