MedicalPatchNet: Model Weights
This repository hosts the pre-trained model weights for MedicalPatchNet and the baseline EfficientNet-B0 model, as described in our paper MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification [TODO ADD LINK].
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
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.
Key Features
- Self-explainable by design: No need for external interpretation methods like Grad-CAM.
- Competitive performance: Achieves comparable classification accuracy to a standard EfficientNet-B0.
- Superior localization: Significantly outperforms Grad-CAM variants in pathology localization tasks.
- Faithful explanations: Saliency maps directly reflect the model's true reasoning.
How to Use These Weights
The weights provided here are intended to be used with the code from our GitHub repository.
Models Included
- MedicalPatchNet: The main patch-based, self-explainable model.
- EfficientNet-B0: The baseline model used for comparison with post-hoc methods (Grad-CAM, Grad-CAM++, and Eigen-CAM).
Citation
If you use MedicalPatchNet or these model weights in your research, please cite our work:
[TODO ADD CITATION]