3D ReX: Causal Explanations in 3D Neuroimaging Classification
Journal:
arXiv
Published Date:
Feb 14, 2025
Abstract
Explainability remains a significant problem for AI models in medical
imaging, making it challenging for clinicians to trust AI-driven predictions.
We introduce 3D ReX, the first causality-based post-hoc explainability tool for
3D models. 3D ReX uses the theory of actual causality to generate
responsibility maps which highlight the regions most crucial to the model's
decision. We test 3D ReX on a stroke detection model, providing insight into
the spatial distribution of features relevant to stroke.
