Interpretability is a crucial factor in building reliable models for various medical applications. Concept Bottleneck Models (CBMs) enable interpretable image classification by utilizing human-understandable concepts as intermediate targets. Unlike conventional methods that require extensive human labor to construct the concept set, recent works leveraging Large Language Models (LLMs) for generating concepts made automatic concept generation possible. However, those methods do not consider whether a concept is visually relevant or not, which is an important factor in computing meaningful concept scores. Therefore, we propose a visual activation score that measures whether the concept contains visual cues or not, which can be easily computed with unlabeled image data. Computed visual activation scores are then used to filter out the less visible concepts, thus resulting in a final concept set with visually meaningful concepts. Our experimental results show that adopting the proposed visual activation score for concept filtering consistently boosts performance compared to the baseline. Moreover, qualitative analyses also validate that visually relevant concepts are successfully selected with the visual activation score.

翻译：可解释性是构建可靠医学模型的关键因素之一。概念瓶颈模型（CBMs）通过利用人类可理解的概念作为中间目标，实现了可解释的图像分类。与需要大量人工构建概念集的传统方法不同，近期利用大语言模型（LLMs）生成概念的研究使得自动概念生成成为可能。然而，这些方法未考虑概念在视觉上是否相关——而这是计算有意义概念得分的重要依据。为此，我们提出一种视觉激活得分，用于衡量概念是否包含视觉线索，该得分可通过未标注图像数据轻松计算。计算出的视觉激活得分随后用于过滤视觉显著性较弱的概念，从而得到由视觉上有意义的概念构成的最终概念集。实验结果表明，与基线方法相比，采用所提出的视觉激活得分进行概念过滤可持续提升模型性能。此外，定性分析也验证了视觉激活得分能够成功筛选出视觉相关的概念。