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---
license: mit
language: en
library_name: pytorch
tags:
- pytorch
- medical-imaging
- chest-x-ray
- explainable-ai
- image-classification
- efficientnet
- MedicalPatchNet
---
# MedicalPatchNet: Model Weights
This repository hosts the pre-trained model weights for **MedicalPatchNet** and the baseline **EfficientNetV2-S** 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](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 EfficientNetV2-S.
- **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](https://github.com/TruhnLab/MedicalPatchNet).
## 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).
---
## Citation
If you use MedicalPatchNet or these model weights in your research, please cite our work:
```bibtex
[TODO ADD CITATION]
``` |