Boosting Medical Image Classification with Segmentation Foundation Model
This work addresses the gap in using SAM for medical image classification, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of adapting the Segment Anything Model (SAM) for medical image classification by introducing SAMAug-C, an augmentation method that generates image variants to boost classification performance, achieving validated effectiveness on three public datasets.
The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images. Recently, SAM has gained a great deal of attention for its applications in medical image segmentation. However, to our best knowledge, no studies have shown how to harness the power of SAM for medical image classification. To fill this gap and make SAM a true ``foundation model'' for medical image analysis, it is highly desirable to customize SAM specifically for medical image classification. In this paper, we introduce SAMAug-C, an innovative augmentation method based on SAM for augmenting classification datasets by generating variants of the original images. The augmented datasets can be used to train a deep learning classification model, thereby boosting the classification performance. Furthermore, we propose a novel framework that simultaneously processes raw and SAMAug-C augmented image input, capitalizing on the complementary information that is offered by both. Experiments on three public datasets validate the effectiveness of our new approach.