{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T12:26:53Z","timestamp":1770380813385,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051684","type":"print"},{"value":"9783032051691","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05169-1_52","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:49:45Z","timestamp":1758318585000},"page":"540-550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SAMed-2: Selective Memory Enhanced Medical Segment Anything Model"],"prefix":"10.1007","author":[{"given":"Zhiling","family":"Yan","sequence":"first","affiliation":[]},{"given":"Sifan","family":"Song","sequence":"additional","affiliation":[]},{"given":"Dingjie","family":"Song","sequence":"additional","affiliation":[]},{"given":"Yiwei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Weixiang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhennong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Sekeun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Quanzheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lifang","family":"He","sequence":"additional","affiliation":[]},{"given":"Lichao","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"1","key":"52_CR1","doi-asserted-by":"publisher","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 4128 (2022)","journal-title":"Nat. Commun."},{"key":"52_CR2","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: Swin-unet: unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer, Heidelberg (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"52_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103310","volume":"98","author":"C Chen","year":"2024","unstructured":"Chen, C., et al.: Ma-sam: modality-agnostic sam adaptation for 3d medical image segmentation. Med. Image Anal. 98, 103310 (2024)","journal-title":"Med. Image Anal."},{"key":"52_CR4","unstructured":"Dai, H., et\u00a0al.: Samaug: point prompt augmentation for segment anything model. arXiv preprint arXiv:2307.01187 (2023)"},{"issue":"3","key":"52_CR5","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.13005\/bpj\/1484","volume":"11","author":"B Goyal","year":"2018","unstructured":"Goyal, B., Agrawal, S., Sohi, B.: Noise issues prevailing in various types of medical images. Biomed. Pharmacol. J. 11(3), 1227 (2018)","journal-title":"Biomed. Pharmacol. J."},{"key":"52_CR6","unstructured":"Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397 (2016)"},{"key":"52_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103061","volume":"92","author":"Y Huang","year":"2024","unstructured":"Huang, Y., et al.: Segment anything model for medical images? Med. Image Anal. 92, 103061 (2024)","journal-title":"Med. Image Anal."},{"issue":"2","key":"52_CR8","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"52_CR9","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"52_CR10","unstructured":"Kumari, P., Chauhan, J., Bozorgpour, A., Huang, B., Azad, R., Merhof, D.: Continual learning in medical image analysis: a comprehensive review of recent advancements and future prospects. arXiv preprint arXiv:2312.17004 (2023)"},{"key":"52_CR11","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, M., Yang, X.: Polyp-sam: transfer sam for polyp segmentation. In: Medical Imaging 2024: Computer-Aided Diagnosis, vol. 12927, pp. 759\u2013765. SPIE (2024)","DOI":"10.1117\/12.3006809"},{"issue":"3","key":"52_CR12","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.3390\/su13031224","volume":"13","author":"X Liu","year":"2021","unstructured":"Liu, X., Song, L., Liu, S., Zhang, Y.: A review of deep-learning-based medical image segmentation methods. Sustainability 13(3), 1224 (2021)","journal-title":"Sustainability"},{"key":"52_CR13","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"1","key":"52_CR14","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"52_CR15","unstructured":"OI, E.: Thyroid Ultrasound Dataset (2023). https:\/\/www.kaggle.com\/datasets\/eiraoi\/thyroidultrasound"},{"key":"52_CR16","unstructured":"O\u2019shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)"},{"key":"52_CR17","unstructured":"Ravi, N., et\u00a0al.: Sam 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)"},{"key":"52_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":"52_CR19","doi-asserted-by":"crossref","unstructured":"Sivaswamy, J., Krishnadas, S., Joshi, G.D., Jain, M., Tabish, A.U.S.: Drishti-gs: retinal image dataset for optic nerve head (onh) segmentation. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 53\u201356. IEEE (2014)","DOI":"10.1109\/ISBI.2014.6867807"},{"key":"52_CR20","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, X., Su, H., Zhu, J.: A comprehensive survey of continual learning: theory, method and application. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"52_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, D.: Customized segment anything model for medical image segmentation. arXiv preprint arXiv:2304.13785 (2023)","DOI":"10.2139\/ssrn.4495221"},{"key":"52_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jiao, R.: Towards segment anything model (sam) for medical image segmentation: a survey. arXiv preprint arXiv:2305.03678 (2023)","DOI":"10.2139\/ssrn.4495221"},{"key":"52_CR23","unstructured":"Zhu, J., Qi, Y., Wu, J.: Medical sam 2: segment medical images as video via segment anything model 2. arXiv preprint arXiv:2408.00874 (2024)"},{"key":"52_CR24","first-page":"19769","volume":"36","author":"X Zou","year":"2023","unstructured":"Zou, X., et al.: Segment everything everywhere all at once. Adv. Neural. Inf. Process. Syst. 36, 19769\u201319782 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05169-1_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:49:51Z","timestamp":1758318591000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05169-1_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051684","9783032051691"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05169-1_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}