{"ID":2872921,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.07477","arxiv_id":"2509.07477","title":"MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification","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 explanations accessible even to non-AI experts, MedicalPatchNet mitigates risks associated with shortcut learning, thus improving clinical trust. Our model is publicly available with reproducible training and inference scripts and contributes to safer, explainable AI-assisted diagnostics across medical imaging domains. We make the code publicly available: https://github.com/TruhnLab/MedicalPatchNet","short_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. Medica...","url_abs":"https://arxiv.org/abs/2509.07477","url_pdf":"https://arxiv.org/pdf/2509.07477v2","authors":"[\"Patrick Wienholt\",\"Christiane Kuhl\",\"Jakob Nikolas Kather\",\"Sven Nebelung\",\"Daniel Truhn\"]","published":"2025-09-09T08:02:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":610007,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2872921,"paper_url":"https://arxiv.org/abs/2509.07477","paper_title":"MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification","repo_url":"https://github.com/TruhnLab/MedicalPatchNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}