  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.
