| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| # MedVision-XRay |
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| <div align="center"> |
| <img src="figures/architecture.png" width="60%" alt="MedVision-XRay" /> |
| </div> |
| <hr> |
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| <div align="center" style="line-height: 1;"> |
| <a href="LICENSE" style="margin: 2px;"> |
| <img alt="License" src="figures/badge.png" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
| |
| ## 1. Introduction |
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| MedVision-XRay is a state-of-the-art deep learning model for multi-label chest X-ray classification. The model has been trained on large-scale medical imaging datasets and can detect 14 different thoracic pathologies including pneumonia, atelectasis, cardiomegaly, and more. |
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| <p align="center"> |
| <img width="80%" src="figures/performance.png"> |
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| The model architecture is based on a modified ResNet backbone with attention mechanisms specifically designed for medical imaging applications. It achieves competitive performance across multiple medical imaging benchmarks. |
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| ## 2. Evaluation Results |
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| ### Comprehensive Benchmark Results |
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| <div align="center"> |
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| | | Benchmark | BaselineModel | ResNet-50 | DenseNet-121 | MedVision-XRay | |
| |---|---|---|---|---|---| |
| | **Detection Tasks** | Pneumonia Detection | 0.821 | 0.845 | 0.856 | 0.840 | |
| | | Atelectasis Detection | 0.756 | 0.778 | 0.785 | 0.809 | |
| | | Cardiomegaly Detection | 0.889 | 0.901 | 0.912 | 0.919 | |
| | **Segmentation Tasks** | Lung Segmentation | 0.912 | 0.925 | 0.931 | 0.934 | |
| | | Lesion Segmentation | 0.782 | 0.801 | 0.815 | 0.811 | |
| | | Cardiac Segmentation | 0.845 | 0.862 | 0.871 | 0.861 | |
| | **Classification Tasks** | Multi-label Classification | 0.723 | 0.751 | 0.768 | 0.775 | |
| | | Severity Grading | 0.678 | 0.701 | 0.712 | 0.761 | |
| | | Finding Localization | 0.645 | 0.678 | 0.692 | 0.688 | |
| | **Robustness Tests** | Noise Resistance | 0.812 | 0.834 | 0.845 | 0.847 | |
| | | Contrast Variation | 0.789 | 0.812 | 0.825 | 0.815 | |
| | | Resolution Invariance | 0.756 | 0.778 | 0.789 | 0.800 | |
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| </div> |
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| ### Overall Performance Summary |
| MedVision-XRay demonstrates strong performance across all evaluated medical imaging benchmark categories, with particularly notable results in detection and segmentation tasks. |
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| ## 3. Model Usage |
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| The model can be loaded using the Hugging Face Transformers library: |
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| ```python |
| from transformers import AutoModelForImageClassification, AutoFeatureExtractor |
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| model = AutoModelForImageClassification.from_pretrained("medical-ai/MedVision-XRay") |
| feature_extractor = AutoFeatureExtractor.from_pretrained("medical-ai/MedVision-XRay") |
| ``` |
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| ## 4. Training Details |
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| The model was trained with the following configuration: |
| - Optimizer: AdamW with learning rate 1e-4 |
| - Batch size: 32 |
| - Image resolution: 224x224 |
| - Augmentations: Random rotation, horizontal flip, color jitter |
| - Training epochs: 50 |
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| ## 5. License |
| This model is licensed under the [Apache 2.0 License](LICENSE). For research and clinical use, please consult with your institution's IRB. |
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| ## 6. Contact |
| If you have any questions, please raise an issue on our GitHub repository or contact us at support@medvision-ai.org. |
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