Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms

Automated breast mass segmentation from mammograms is crucial for assisting radiologists in timely and accurate breast cancer diagnosis. Segment Anything Model (SAM) has recently demonstrated remarkable success in natural image segmentation, suggesting its potential for enhancing artificial intelligence-based automated diagnostic systems. Unfortunately, we observe that the zero-shot performance of SAM in mass segmentation falls short of usability. Therefore, fine-tuning SAM for transfer learning is necessary. However, full-tuning is cost-intensive for foundation models, making it unacceptable in clinical practice. To tackle this problem, in this paper, we propose a parameter-efficient fine-tuning framework named Mammo-SAM, which significantly improves the performance of SAM on the challenging task of mass segmentation. Our key insight includes a tailored adapter to explore multi-scale features and a re-designed CNN-style decoder for precise segmentation. Extensive experiments on the public datasets CBIS-DDSM and INbreast demonstrate that our proposed Mammo-SAM surpasses existing mass segmentation methods and other tuning paradigms designed for SAM, achieving new state-of-the-art performance.