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[2509.07477] MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Summary
MedicalPatchNet introduces a self-explainable AI architecture for chest X-ray classification, enhancing interpretability while maintaining high classification performance.
Why It Matters
The development of MedicalPatchNet addresses the critical challenge of interpretability in AI-driven medical diagnostics. By providing clear explanations of its decision-making process, it enhances clinical trust and safety, making AI tools more acceptable in healthcare settings.
Key Takeaways
- MedicalPatchNet improves interpretability in AI for chest X-rays.
- It matches the performance of existing models while providing clearer diagnostic insights.
- The architecture allows for intuitive visualization of diagnostic contributions from image patches.
- Public availability of the model promotes reproducibility and further research.
- Enhanced interpretability could lead to greater acceptance of AI in clinical environments.
Computer Science > Computer Vision and Pattern Recognition arXiv:2509.07477 (cs) [Submitted on 9 Sep 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification Authors:Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn View a PDF of the paper titled MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification, by Patrick Wienholt and 4 other authors View PDF HTML (experimental) Abstract:Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance. We present MedicalPatchNet, an inherently self-explainable architecture for chest X-ray classification that transparently attributes decisions to distinct image regions. MedicalPatchNet splits images into non-overlapping patches, independently classifies each patch, and aggregates predictions, enabling intuitive visualization of each patch's diagnostic contribution without post-hoc techniques. Trained on the CheXpert dataset (223,414 images), MedicalPatchNet matches the classification performance (AUROC 0.907 vs. 0.908) of EfficientNetV2-S, while improving interpretability: MedicalPatchNet demonstrates improved interpretability with higher pathology localization accuracy (mean hit-rate 0.485 vs. 0.376 with Grad-CAM) on the CheXlocalize dataset. By providing explicit, reliable explanatio...