RE-SAM2: Boosting Few-Shot Medical Image Segmentation via Reinforcement Learning and Ensemble Learning

Abstract

Deep learning models for medical image segmentation often encounter difficulties when there is a lack of annotated data. While current few-shot segmentation methods have reduced these challenges, they frequently fail to fully utilize the information in the limited samples available. Additionally, they typically depend on large quantities of unlabeled data with pseudo-labels for domain adaptation. In response to these issues, we propose RE-SAM2, a novel framework for fewshot medical image segmentation that combines reinforcement learning with ensemble learning. The central concept involves retaining the reward model from reinforcement learning after training and integrating it into the model through ensemble learning techniques. Unlike previous methods, RE-SAM2 does not require extra unlabeled data and achieves notable improvements in segmentation accuracy with limited supervision. Experiments conducted on benchmark datasets reveal that RE-SAM2 surpasses current leading approaches. The code is available at the link https://github.com/zzzzz37/RE-SAM2.

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