For the complete source code, documentation, and instructions on how to train and evaluate the models, please visit our main GitHub repository:
MedicalPatchNet is a self-explainable deep learning architecture designed for chest X-ray classification that provides transparent and interpretable predictions without relying on post-hoc explanation methods. Unlike traditional black-box models that require external tools like Grad-CAM for interpretability, MedicalPatchNet integrates explainability directly into its architectural design.
The architecture divides images into non-overlapping patches, independently classifies each patch using an EfficientNetV2-S backbone, and aggregates predictions through averaging. This enables intuitive visualization of each patch's diagnostic contribution.
The weights provided here are intended to be used with the code from our
GitHub repository. The repository includes scripts for data preprocessing, training, and evaluation.
If you use MedicalPatchNet or these model weights in your research, please cite our work:
1@article{wienholt2026medicalpatchnet,
2 title={MedicalPatchNet: a patch-based self-explainable AI architecture for chest X-ray classification},
3 author={Wienholt, Patrick and Kuhl, Christiane and Kather, Jakob Nikolas and Nebelung, Sven and Truhn, Daniel},
4 journal={Scientific Reports},
5 year={2026},
6 publisher={Nature Publishing Group UK London}
7}
