3D ReX: Causal Explanations in 3D Neuroimaging Classification
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 14 Feb 2025.
Builds on
6 of 20 references.
References · 6
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
- 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 612
- 2020Domain aware medical image classifier interpretation by counterfactual impact analysiscited by 17
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Citing papers per year: 2026: 2.
2 papers.