Segmentation Foundation Model-Aided Medical Image Segmentation

Accurate medical image segmentation is significant for reliable clinical diagnoses and pathology research. Deep learning methods rely on large high-quality dataset for supervised training to achieve satisfactory performance, but manually annotating large-scale datasets is time-consuming and costly. A recent breakthrough in the segmentation foundation model SAM shows promise for aiding annotation. However, when using SAM for annotation, errors are inevitably introduced. To utilize SAM for effective labeling, we adopt a dual-stream collaborative learning framework. Initially, a subset from the dataset is divided and then SAM is used to generate noisy labels. The first branch incorporates a dynamic weight fusion module, which adaptively fuses complementary features from model predictions and noisy labels, reconstructing more informative labels. The second branch aims to improve segmentation accuracy by training the model on an accurate subset and sharing parameters with the other branch. Our method outperforms state-of-the-art segmentation methods on the ISIC2018 and BUSI datasets.