将胸部X光片分块分类，自动标注诊断依据，让AI决策可看懂。
MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
- 图像切块独立分类，聚合结果生成可解释决策
- 在CheXpert上性能媲美EfficientNetV2-S（AUROC 0.907）
- 适合临床医生快速理解AI判断，提升对模型的信任
深度神经网络在放射影像分类中表现优异，但可解释性差，限制了临床应用。本文提出MedicalPatchNet，一种基于图像块的自解释架构，能透明地将分类决策归因于具体图像区域。该模型将胸片分割为非重叠块，独立分类每个块并聚合预测结果，无需事后解释技术即可直观展示各块的诊断贡献。在包含223,414张图像的CheXpert数据集上训练，MedicalPatchNet的分类性能（AUROC 0.907）与EfficientNetV2-S相当（0.908），同时显著提升可解释性：在CheXlocalize数据集上，病理定位准确率（平均命中率0.485）优于Grad-CAM（0.376）。通过提供清晰、可靠的解释，即使非AI专家也能理解模型判断，缓解捷径学习风险，增强临床信任。代码已公开，支持可复现训练与推理，推动医疗影像领域更安全、可解释的AI辅助诊断。
原文摘要 · Abstract (English)
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
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