Create README.md

README.md

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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 **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/paddyOnGithub/MedicalPatchNet](https://github.com/paddyOnGithub/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/paddyOnGithub/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]
+```