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--- |
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license: mit |
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language: en |
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library_name: pytorch |
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tags: |
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- pytorch |
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- medical-imaging |
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- chest-x-ray |
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- explainable-ai |
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- image-classification |
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- efficientnet |
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- MedicalPatchNet |
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--- |
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# MedicalPatchNet: Model Weights |
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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]. |
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For the complete source code, documentation, and instructions on how to train and evaluate the models, please visit our main GitHub repository: |
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**[https://github.com/TruhnLab/MedicalPatchNet](https://github.com/TruhnLab/MedicalPatchNet)** |
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--- |
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## Overview |
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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. |
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### Key Features |
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- **Self-explainable by design**: No need for external interpretation methods like Grad-CAM. |
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- **Competitive performance**: Achieves comparable classification accuracy to a standard EfficientNet-B0. |
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- **Superior localization**: Significantly outperforms Grad-CAM variants in pathology localization tasks. |
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- **Faithful explanations**: Saliency maps directly reflect the model's true reasoning. |
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--- |
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## How to Use These Weights |
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The weights provided here are intended to be used with the code from our [GitHub repository](https://github.com/TruhnLab/MedicalPatchNet). |
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## Models Included |
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- **MedicalPatchNet**: The main patch-based, self-explainable model. |
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- **EfficientNet-B0**: The baseline model used for comparison with post-hoc methods (Grad-CAM, Grad-CAM++, and Eigen-CAM). |
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--- |
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## Citation |
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If you use MedicalPatchNet or these model weights in your research, please cite our work: |
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```bibtex |
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[TODO ADD CITATION] |
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``` |
