Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models

• URL: http://arxiv.org/abs/2311.14339v2
• Date: Wed, 6 Mar 2024 14:23:38 GMT
• Title: Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models
• Authors: Cristiano Patr\'icio, Lu\'is F. Teixeira, Jo\~ao C. Neves
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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