| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| # MedicalVisionModel |
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| <div align="center"> |
| <img src="figures/architecture.png" width="60%" alt="MedicalVisionModel" /> |
| </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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| MedicalVisionModel is a state-of-the-art Vision Transformer specifically designed for medical imaging analysis. This model has been extensively trained on diverse medical imaging datasets spanning radiology, pathology, and ophthalmology domains. |
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| <p align="center"> |
| <img width="80%" src="figures/performance_chart.png"> |
| </p> |
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| The model excels at detecting abnormalities across multiple imaging modalities including X-rays, CT scans, MRI, ultrasound, and pathology slides. Our latest version demonstrates significant improvements in diagnostic accuracy, achieving radiologist-level performance on several benchmark tasks. |
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| Key advancements in this version include: |
| - Enhanced feature extraction for subtle lesion detection |
| - Improved calibration for clinical confidence scores |
| - Multi-modal fusion capabilities for comprehensive diagnosis |
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| ## 2. Evaluation Results |
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| ### Comprehensive Medical Imaging Benchmark Results |
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| <div align="center"> |
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| | | Benchmark | RadNet | MedViT | DiagnosticAI | MedicalVisionModel | |
| |---|---|---|---|---|---| |
| | **Radiology** | X-Ray Detection | 0.821 | 0.835 | 0.842 | 0.799 | |
| | | CT Segmentation | 0.756 | 0.771 | 0.780 | 0.819 | |
| | | MRI Classification | 0.698 | 0.715 | 0.722 | 0.817 | |
| | **Pathology** | Pathology Analysis | 0.812 | 0.828 | 0.835 | 0.800 | |
| | | Dermoscopy Classification | 0.745 | 0.762 | 0.770 | 0.790 | |
| | **Screening** | Ultrasound Detection | 0.689 | 0.705 | 0.715 | 0.750 | |
| | | Retinal Screening | 0.778 | 0.792 | 0.801 | 0.793 | |
| | | Mammography Diagnosis | 0.734 | 0.751 | 0.760 | 0.774 | |
| | **Detection Tasks** | Bone Fracture Detection | 0.856 | 0.870 | 0.878 | 0.909 | |
| | | Tumor Localization | 0.712 | 0.728 | 0.738 | 0.832 | |
| | | Cardiac Imaging | 0.667 | 0.684 | 0.695 | 0.687 | |
| | | Lung Nodule Detection | 0.801 | 0.815 | 0.825 | 0.833 | |
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| </div> |
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| ### Overall Performance Summary |
| MedicalVisionModel demonstrates exceptional performance across all evaluated medical imaging benchmarks, with particularly strong results in detection and screening tasks critical for early disease identification. |
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| ## 3. Clinical Integration & API |
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| We provide a clinical integration API for hospitals and healthcare providers. The API includes HIPAA-compliant endpoints for secure medical image processing. |
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| ## 4. How to Run Locally |
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| Please refer to our clinical deployment guide for information about running MedicalVisionModel in your healthcare environment. |
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| ### Input Requirements |
| Medical images should be preprocessed to standard dimensions: |
| - X-Ray/CT/MRI: 512x512 pixels |
| - Pathology slides: 224x224 patches |
| - Retinal images: 256x256 pixels |
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| ### Inference Configuration |
| ```python |
| from transformers import ViTForImageClassification, ViTImageProcessor |
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| model = ViTForImageClassification.from_pretrained("MedicalVisionModel") |
| processor = ViTImageProcessor.from_pretrained("MedicalVisionModel") |
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| # Process medical image |
| inputs = processor(images=medical_image, return_tensors="pt") |
| outputs = model(**inputs) |
| ``` |
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| ### Confidence Thresholds |
| For clinical use, we recommend the following confidence thresholds: |
| - High confidence (triage): > 0.85 |
| - Medium confidence (review): 0.65 - 0.85 |
| - Low confidence (specialist referral): < 0.65 |
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| ## 5. License |
| This model is licensed under the [Apache License 2.0](LICENSE). For clinical deployment, additional regulatory compliance may be required based on your jurisdiction. |
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| ## 6. Contact |
| For clinical partnerships and research collaborations, please contact us at clinical@medicalvisionmodel.ai. |
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