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Acknowledging this growing interest, we review the literature on few-shot medical image segmentation from 2020 to early 2025, focusing on architectural modifications, loss-inspired learning strategies, and meta-learning frameworks. We further divide each category into fine-grained deep learning-oriented solutions, including self-supervised learning, contrastive learning, regularization, and foundation models providing in-depth discussions on architectural improvements and representation learning strategies. Additionally, we present preliminary results from several few-shot segmentation models across both medical and computer vision domains, evaluating their strengths and limitations for medical image applications. Finally, based on the limitations observed, advancements from the natural image domain, and empirical findings, we outline future research directions, providing specific insights into data-efficient learning, rapid adaptation of foundation models and generalization. The code is available here.<\/jats:p>","DOI":"10.1145\/3746224","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T07:11:23Z","timestamp":1751008283000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9741-1575","authenticated-orcid":false,"given":"Theekshana","family":"Dissanayake","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Robotics, Queensland University of Technology","place":["Brisbane, Australia"]},{"name":"Faculty of Information Technology, Monash University","place":["Brisbane, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0977-0656","authenticated-orcid":false,"given":"Yasmeen","family":"George","sequence":"additional","affiliation":[{"name":"Monash University Faculty of Information Technology","place":["Clayton, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9749-7858","authenticated-orcid":false,"given":"Dwarikanath","family":"Mahapatra","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University","place":["Clayton, Australia"]},{"name":"Department of Computer Science, Khalifa University","place":["Clayton, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4316-9001","authenticated-orcid":false,"given":"Shridha","family":"Sridharan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics, Queensland University of Technology","place":["Brisbane, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8515-6324","authenticated-orcid":false,"given":"Clinton","family":"Fookes","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics, Queensland University of Technology","place":["Brisbane, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5880-8673","authenticated-orcid":false,"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University","place":["Clayton, Australia"]}]}],"member":"320","published-online":{"date-parts":[[2025,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0734-189X(85)90153-7"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/42.996338"},{"key":"e_1_3_3_4_2","first-page":"5184","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","author":"Aleem Sidra","year":"2024","unstructured":"Sidra Aleem, Fangyijie Wang, Mayug Maniparambil, Eric Arazo, Julia Dietlmeier, Kathleen Curran, Noel E. 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