Transparent and Clinically Interpretable AI for Lung Cancer Detection in
  Chest X-Rays
Amy Rafferty Rishi Ramaesh Ajitha Rajan
The rapidly advancing field of Explainable Artificial Intelligence (XAI) aims
to tackle the issue of trust regarding the use of complex black-box deep
learning models in real-world applications. Existing post-hoc XAI techniques
have recently been shown to have poor performance on medical data, producing
unreliable explanations which are infeasible for clinical use. To address this,
we propose an ante-hoc approach based on concept bottleneck models which
introduces for the first time clinical concepts into the classification
pipeline, allowing the user valuable insight into the decision-making process.
On a large public dataset of chest X-rays and associated medical reports, we
focus on the binary classification task of lung cancer detection. Our approach
yields improved classification performance in lung cancer detection when
compared to baseline deep learning models (F1 > 0.9), while also generating
clinically relevant and more reliable explanations than existing techniques. We
evaluate our approach against post-hoc image XAI techniques LIME and SHAP, as
well as CXR-LLaVA, a recent textual XAI tool which operates in the context of
question answering on chest X-rays.