By learning to pair dermatological images and related concepts in a self-supervised manner, a visual-language foundation model is shown to have comparable performance to supervised models for concept annotation and is used to scrutinize model decisions for enhanced interpretability and accountability of medical imaging applications.
- Chanwoo Kim
- Soham U. Gadgil
- Su-In Lee
