  Semi-supervised learning (SSL) has achieved notable progress in medical image
segmentation. To achieve effective SSL, a model needs to be able to efficiently
learn from limited labeled data and effectively exploiting knowledge from
abundant unlabeled data. Recent developments in visual foundation models, such
as the Segment Anything Model (SAM), have demonstrated remarkable adaptability
with improved sample efficiency. To harness the power of foundation models for
application in SSL, we propose a cross prompting consistency method with
segment anything model (CPC-SAM) for semi-supervised medical image
segmentation. Our method employs SAM's unique prompt design and innovates a
cross-prompting strategy within a dual-branch framework to automatically
generate prompts and supervisions across two decoder branches, enabling
effectively learning from both scarce labeled and valuable unlabeled data. We
further design a novel prompt consistency regularization, to reduce the prompt
position sensitivity and to enhance the output invariance under different
prompts. We validate our method on two medical image segmentation tasks. The
extensive experiments with different labeled-data ratios and modalities
demonstrate the superiority of our proposed method over the state-of-the-art
SSL methods, with more than 9% Dice improvement on the breast cancer
segmentation task.
