Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study

作     者：Theekshana Dissanayake Yasmeen George Dwarikanath Mahapatra Shridha Sridharan Clinton Fookes Zongyuan Ge 

作者机构：School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia Faculty of Information Technology Monash University Clayton Australia Monash University Faculty of Information Technology Clayton Australia Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates 

出 版 物：《ACM Computing Surveys》 

年 卷 期：1000年

主　　题：Segmentation Medical Image Few-Shot Learning Deep Learning 

摘      要：Medical image segmentation plays a crucial role in assisting clinicians with diagnosing critical medical conditions. In deep learning, few-shot learning methods aim to replicate human learning by leveraging fewer examples for determining a prediction for a novel class. Researchers in the medical imaging community have also explored novel methods for few-shot medical image segmentation, leveraging meta-learning, foundation models and self-supervised learning (SSL). 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.