---
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 **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](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](https://github.com/TruhnLab/MedicalPatchNet).

## 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:

```bibtex
[TODO ADD CITATION]
```