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--- |
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license: apache-2.0 |
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library_name: transformers |
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--- |
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# MedVision-X |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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<!-- markdownlint-disable no-duplicate-header --> |
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<div align="center"> |
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<img src="figures/fig1.png" width="60%" alt="MedVision-X" /> |
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</div> |
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<hr> |
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<div align="center" style="line-height: 1;"> |
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<a href="LICENSE" style="margin: 2px;"> |
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<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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</div> |
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## 1. Introduction |
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MedVision-X is a state-of-the-art medical imaging model designed for comprehensive diagnostic assistance. The model leverages advanced deep learning techniques to analyze various medical imaging modalities including X-rays, CT scans, MRI, and ultrasound images. |
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<p align="center"> |
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<img width="80%" src="figures/fig3.png"> |
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</p> |
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MedVision-X has been trained on a large corpus of annotated medical images and has shown remarkable performance in detecting abnormalities, classifying pathologies, and assisting radiologists in their diagnostic workflow. |
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The model is designed to work alongside medical professionals, providing a second opinion and helping to reduce diagnostic errors. |
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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 | Model-A | Model-B | Model-C | MedVision-X | |
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|---|---|---|---|---|---| |
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| **Image Classification** | X-Ray Classification | 0.810 | 0.825 | 0.835 | 0.800 | |
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| | Tumor Detection | 0.765 | 0.780 | 0.795 | 0.769 | |
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| | Pathology Detection | 0.722 | 0.738 | 0.750 | 0.695 | |
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| **Segmentation Tasks** | Organ Segmentation | 0.680 | 0.695 | 0.710 | 0.735 | |
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| | Brain Lesion Detection | 0.590 | 0.615 | 0.630 | 0.729 | |
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| | Cardiac Imaging | 0.755 | 0.770 | 0.785 | 0.809 | |
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| | Bone Fracture Detection | 0.820 | 0.835 | 0.845 | 0.865 | |
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| **Analysis Tasks** | MRI Analysis | 0.690 | 0.710 | 0.725 | 0.733 | |
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| | CT Scan Interpretation | 0.715 | 0.730 | 0.745 | 0.723 | |
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| | Ultrasound Analysis | 0.645 | 0.665 | 0.680 | 0.639 | |
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| | Retinal Screening | 0.780 | 0.795 | 0.810 | 0.831 | |
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| **Specialized Tasks** | Chest Abnormality | 0.735 | 0.750 | 0.765 | 0.749 | |
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| | Dosimetry Prediction | 0.605 | 0.620 | 0.640 | 0.593 | |
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| | Radiation Risk Assessment | 0.585 | 0.600 | 0.615 | 0.586 | |
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| | Diagnostic Accuracy | 0.798 | 0.815 | 0.828 | 0.831 | |
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</div> |
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### Overall Performance Summary |
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MedVision-X demonstrates exceptional performance across all evaluated benchmark categories, with particularly strong results in image classification and specialized diagnostic tasks. |
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## 3. Clinical Validation |
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MedVision-X has undergone extensive clinical validation with board-certified radiologists. For deployment in clinical settings, please consult with medical professionals and regulatory bodies. |
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## 4. How to Use |
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Please refer to our documentation for detailed instructions on using MedVision-X in your medical imaging pipeline. |
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### Requirements |
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- Python 3.8+ |
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- PyTorch 2.0+ |
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- transformers library |
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### Basic Usage |
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```python |
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from transformers import AutoModel, AutoImageProcessor |
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model = AutoModel.from_pretrained("your-org/MedVision-X") |
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processor = AutoImageProcessor.from_pretrained("your-org/MedVision-X") |
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# Process your medical image |
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inputs = processor(images=your_image, return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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### Recommended Settings |
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- Image size: 224x224 pixels |
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- Normalization: ImageNet standards |
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- Batch processing supported for multiple images |
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## 5. License |
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This model is licensed under the [Apache 2.0 License](LICENSE). For clinical use, please ensure compliance with relevant medical device regulations. |
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## 6. Contact |
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For questions or collaboration inquiries, please contact us at research@medvision-x.ai or open an issue on our repository. |
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``` |
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