SAM-ALE: Enhancing SAM for Low-Shot Digital Pathology Semantic Segmentation Via an Auxiliary Lightweight Encoder

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The Segment Anything Model (SAM) has been introduced for universal segmentation in natural images but struggles with medical image segmentation due to a significant domain gap. While various medical SAM variants have been developed to adapt SAM to medical datasets, we observe that they still require a large amount of labeled data to perform effectively in digital pathology semantic segmentation, where annotations are particularly costly and time-consuming. To address this challenge, we propose SAM-ALE, a novel approach that enhances SAM for Low-shot digital pathology semantic segmentation via an Auxiliary Lightweight Encoder. Our approach leverages a small amount of labeled data and a large pool of unlabeled data to train a task-specific lightweight encoder, which captures pathology-specific visual features. The encoded features are then integrated with SAM’s embeddings to improve segmentation performance with minimal labeled supervision. Experiments on the CRAG and MoNuSeg datasets validate our approach. For example, with just 2% labeled data, our method enables SAM to achieve performance (86.52% Dice) comparable to its fully fine-tuned counterpart (87.37% Dice) on CRAG, utilizing a lightweight encoder with only 7.7M parameters. Additionally, our approach allows seamless integration with various medical SAM variants. The code and data are released at https://github.com/qianyuli123/SAM-ALE.

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