Robust and Interpretable Medical Image Classifiers via Concept
  Bottleneck Models
An Yan Yu Wang Yiwu Zhong Zexue He Petros Karypis Zihan Wang Chengyu Dong Amilcare Gentili Chun-Nan Hsu Jingbo Shang Julian McAuley
Medical image classification is a critical problem for healthcare, with the
potential to alleviate the workload of doctors and facilitate diagnoses of
patients. However, two challenges arise when deploying deep learning models to
real-world healthcare applications. First, neural models tend to learn spurious
correlations instead of desired features, which could fall short when
generalizing to new domains (e.g., patients with different ages). Second, these
black-box models lack interpretability. When making diagnostic predictions, it
is important to understand why a model makes a decision for trustworthy and
safety considerations. In this paper, to address these two limitations, we
propose a new paradigm to build robust and interpretable medical image
classifiers with natural language concepts. Specifically, we first query
clinical concepts from GPT-4, then transform latent image features into
explicit concepts with a vision-language model. We systematically evaluate our
method on eight medical image classification datasets to verify its
effectiveness. On challenging datasets with strong confounding factors, our
method can mitigate spurious correlations thus substantially outperform
standard visual encoders and other baselines. Finally, we show how
classification with a small number of concepts brings a level of
interpretability for understanding model decisions through case studies in real
medical data.