{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:44:16Z","timestamp":1780764256476,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734700","type":"print"},{"value":"9783031734717","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T00:00:00Z","timestamp":1727481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T00:00:00Z","timestamp":1727481600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73471-7_11","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T08:02:17Z","timestamp":1727856137000},"page":"103-112","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Lara","family":"Siblini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6499-5655","authenticated-orcid":false,"given":"Gustavo","family":"Andrade-Miranda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamilia","family":"Taguelmimt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Visvikis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Bert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,28]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Andrade-Miranda, G., Jaouen, V., Tankyevych, O., Cheze Le Rest, C., Visvikis, D., Conze, P.H.: Multi-modal medical transformers: A meta-analysis for medical image segmentation in oncology. Computerized Medical Imaging and Graphics 110, 102308 (2023)","DOI":"10.1016\/j.compmedimag.2023.102308"},{"key":"11_CR2","unstructured":"Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., Tian, Y., Schwarzschild, A., Wilson, A.G., Geiping, J., Garrido, Q., Fernandez, P., Bar, A., Pirsiavash, H., LeCun, Y., Goldblum, M.: A cookbook of self-supervised learning (2023)"},{"issue":"6","key":"11_CR3","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TRPMS.2023.3265863","volume":"7","author":"PH Conze","year":"2023","unstructured":"Conze, P.H., Andrade-Miranda, G., Singh, V.K., Jaouen, V., Visvikis, D.: Current and emerging trends in medical image segmentation with deep learning. IEEE Transactions on Radiation and Plasma Medical Sciences 7(6), 545\u2013569 (2023)","journal-title":"IEEE Transactions on Radiation and Plasma Medical Sciences"},{"key":"11_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105012","volume":"146","author":"A Davila","year":"2024","unstructured":"Davila, A., Colan, J., Hasegawa, Y.: Comparison of fine-tuning strategies for transfer learning in medical image classification. Image Vis. Comput. 146, 105012 (2024)","journal-title":"Image Vis. Comput."},{"key":"11_CR5","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"11_CR6","unstructured":"Dutt, R., Ericsson, L., Sanchez, P., Tsaftaris, S.A., Hospedales, T.: Parameter-efficient fine-tuning for medical image analysis: The missed opportunity. In: Medical Imaging with Deep Learning (2024)"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Garret, G., Vacavant, A., Frindel, C.: Deep vessel segmentation based on a new combination of vesselness filters. ArXiv abs\/2402.14509 (2024)","DOI":"10.1109\/ISBI56570.2024.10635696"},{"key":"11_CR8","unstructured":"Gu, H., Dong, H., Yang, J., Mazurowski, M.A.: How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with segment anything model (2024)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H., Xu, D.: Swin UNETR: Swin Transformers for semantic segmentation of brain tumors in MRI images. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (2022)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: UNETR: Transformers for 3D medical image segmentation. In: IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 272\u2013284 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"issue":"2","key":"11_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2020","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.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 (2020)","journal-title":"Nat. Methods"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Doll\u00e1r, P., Girshick, R.: Segment anything. arXiv:2304.02643 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"11_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems. vol.\u00a025. Curran Associates, Inc. (2012)"},{"key":"11_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918","volume":"89","author":"MA Mazurowski","year":"2023","unstructured":"Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: An experimental study. Med. Image Anal. 89, 102918 (2023)","journal-title":"Med. Image Anal."},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","volume":"616","author":"M Moor","year":"2023","unstructured":"Moor, M., Banerjee, O., Abad, Z.S.H., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259\u2013265 (2023)","journal-title":"Nature"},{"key":"11_CR16","unstructured":"Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019)"},{"key":"11_CR17","unstructured":"Saha, A., Bosma, J., Twilt, J., van Ginneken, B., Yakar, D., Elschot, M., Veltman, J., F\u00fctterer, J., de\u00a0Rooij, M., henkjan huisman: Artificial intelligence and radiologists at prostate cancer detection in MRI \u2014 the PI-CAI challenge. In: Medical Imaging with Deep Learning, short paper track (2023)"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Sall\u00e9, G., Andrade-Miranda, G., Conze, P.H., Boussion, N., Bert, J., Visvikis, D., Jaouen, V.: Cross-modal tumor segmentation using generative blending augmentation and self-training. IEEE Transactions on Biomedical Engineering pp. 1\u201312 (2024)","DOI":"10.1109\/TBME.2024.3384014"},{"key":"11_CR19","unstructured":"Wald, T., Roy, S., Koehler, G., Disch, N., Rokuss, M.R., Holzschuh, J., Zimmerer, D., Maier-Hein, K.: Sam.md: Zero-shot medical image segmentation capabilities of the segment anything model. In: Medical Imaging with Deep Learning, short paper track (2023)"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"21300","DOI":"10.1109\/ACCESS.2023.3249759","volume":"11","author":"F Yang","year":"2023","unstructured":"Yang, F., Zamzmi, G., Angara, S., Rajaraman, S., Aquilina, A., Xue, Z., Jaeger, S., Papagiannakis, E., Antani, S.K.: Assessing inter-annotator agreement for medical image segmentation. IEEE Access 11, 21300\u201321312 (2023). epub 2023 Feb 27","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Foundation Models for General Medical AI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73471-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T08:03:41Z","timestamp":1727856221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73471-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,28]]},"ISBN":["9783031734700","9783031734717"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73471-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,28]]},"assertion":[{"value":"28 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MedAGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Foundation Models for General Medical AI","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"medagi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/medagi.github.io\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}