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.

翻译：概念模型天然适用于构建内在可解释的皮肤病变诊断系统，因为医学专家正是基于病灶的一系列视觉模式进行决策。然而，此类模型的开发高度依赖概念标注数据集，而标注过程所需的专业知识和临床经验导致这类数据极为稀缺。本文研究表明，视觉-语言模型可有效缓解对大量概念标注样本的依赖。具体而言，我们提出一种嵌入学习策略，使CLIP模型适应皮肤病变分类的下游任务，将基于概念的文本描述作为文本嵌入。实验结果表明，视觉-语言模型不仅在使用概念作为文本嵌入时获得更高准确率，而且所需的概念标注样本数量更少，就能达到专门为自动概念生成设计的方案的性能水平。