MedicalPatchNet: Model Weights

This repository hosts the pre-trained model weights for MedicalPatchNet and the baseline EfficientNetV2-S model, as described in the paper:

MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification (Nature Scientific Reports, 2026).
Preprint available on arXiv:2509.07477.

For the complete source code, documentation, and instructions on how to train and evaluate the models, please visit our main GitHub repository:

https://github.com/TruhnLab/MedicalPatchNet

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Overview

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.

Key Features

• Self-explainable by design: No need for external interpretation methods like Grad-CAM.
• Competitive performance: Matches the classification performance (AUROC 0.907 vs. 0.908) of EfficientNetV2-S.
• Superior localization: Significantly outperforms Grad-CAM variants in pathology localization tasks (mean hit-rate 0.485 vs. 0.376) on the CheXlocalize dataset.
• Faithful explanations: Saliency maps directly reflect the model's true reasoning, mitigating risks associated with shortcut learning.

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How to Use These Weights

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.

Models Included

• MedicalPatchNet: The main patch-based, self-explainable model.
• EfficientNetV2-S: The baseline model used for comparison with post-hoc methods (Grad-CAM, Grad-CAM++, and Eigen-CAM).

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Citation

If you use MedicalPatchNet or these model weights in your research, please cite our work:

@article{wienholt2026medicalpatchnet,
  title={MedicalPatchNet: a patch-based self-explainable AI architecture for chest X-ray classification},
  author={Wienholt, Patrick and Kuhl, Christiane and Kather, Jakob Nikolas and Nebelung, Sven and Truhn, Daniel},
  journal={Scientific Reports},
  year={2026},
  publisher={Nature Publishing Group UK London}
}