Concept Bottleneck with Visual Concept Filtering for Explainable Medical
  Image Classification
Injae Kim Jongha Kim Joonmyung Choi Hyunwoo J. Kim
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.