InteractMatch: Segment Anything Model with Interact-Consistency for Semi-Supervised Medical Image Segmentation

The scarcity of labeled data is a significant challenge in medical image segmentation tasks. In recent years, Segment Anything Model SAM has gained attention as a foundational model for segmentation tasks due to its powerful zero-shot capabilities and prompt-based interactive manner. However, due to the substantial domain gap between medical and natural image data, adapting SAM to the medical domain requires a large volume of annotated medical data. Unfortu-nately, in medical applications, obtaining densely annotated data is both costly and challenging, particularly for rare diseases. Therefore, to efficiently fine-tune SAM, we consider utilizing Semi-Supervised Learning SSL to harvest knowledge from unlabeled samples. In this paper, we present InteractMatch, which consists of a Prompt Augmentation-Based Consistency PAC and a Cross-Model Knowledge Distillation CKD . The PAC module effectively leverages various types of prompts from SAM to facilitate model training on unlabeled data, improving both robustness and predictive accuracy by introducing perturbations to the prompts. Additionally, CKD is introduced to align the probability distributions of the two model branches, thereby reducing discrepancies in their predictions and enhancing the output invariance of the model. Extensive experiments on two public datasets demonstrate that our InteractMatch achieves state-of-the-art performance in semi-supervised medical image segmentation task, particularly, leading a 1.93 dice score improvement on the ACDC dataset.