MVP-CBM:Multi-layer Visual Preference-enhanced Concept Bottleneck Model for Explainable Medical Image Classification
Journal:
arXiv
Published Date:
Jun 14, 2025
Abstract
The concept bottleneck model (CBM), as a technique improving interpretability
via linking predictions to human-understandable concepts, makes high-risk and
life-critical medical image classification credible. Typically, existing CBM
methods associate the final layer of visual encoders with concepts to explain
the model's predictions. However, we empirically discover the phenomenon of
concept preference variation, that is, the concepts are preferably associated
with the features at different layers than those only at the final layer; yet a
blind last-layer-based association neglects such a preference variation and
thus weakens the accurate correspondences between features and concepts,
impairing model interpretability. To address this issue, we propose a novel
Multi-layer Visual Preference-enhanced Concept Bottleneck Model (MVP-CBM),
which comprises two key novel modules: (1) intra-layer concept preference
modeling, which captures the preferred association of different concepts with
features at various visual layers, and (2) multi-layer concept sparse
activation fusion, which sparsely aggregates concept activations from multiple
layers to enhance performance. Thus, by explicitly modeling concept
preferences, MVP-CBM can comprehensively leverage multi-layer visual
information to provide a more nuanced and accurate explanation of model
decisions. Extensive experiments on several public medical classification
benchmarks demonstrate that MVP-CBM achieves state-of-the-art accuracy and
interoperability, verifying its superiority. Code is available at
https://github.com/wcj6/MVP-CBM.
