Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models
This work addresses the challenge of interpretable AI for medical experts in dermatology, though it is incremental as it adapts existing vision-language models to a specific domain.
The paper tackled the problem of concept-based interpretability in skin lesion diagnosis by reducing the need for large concept-annotated datasets, showing that vision-language models using concept-based descriptions achieve better accuracy and require fewer annotated samples.
Concept-based models naturally lend themselves to the development of inherently interpretable skin lesion diagnosis, as medical experts make decisions based on a set of visual patterns of the lesion. Nevertheless, the development of these models depends on the existence of concept-annotated datasets, whose availability is scarce due to the specialized knowledge and expertise required in the annotation process. In this work, we show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples. In particular, we propose an embedding learning strategy to adapt CLIP to the downstream task of skin lesion classification using concept-based descriptions as textual embeddings. Our experiments reveal that vision-language models not only attain better accuracy when using concepts as textual embeddings, but also require a smaller number of concept-annotated samples to attain comparable performance to approaches specifically devised for automatic concept generation.