| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| # MedVisionAI |
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| <div align="center"> |
| <img src="figures/fig1.png" width="60%" alt="MedVisionAI" /> |
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| <hr> |
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| <div align="center" style="line-height: 1;"> |
| <a href="LICENSE" style="margin: 2px;"> |
| <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
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| ## 1. Introduction |
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| MedVisionAI represents a breakthrough in medical imaging analysis. This latest release incorporates advanced deep learning architectures specifically designed for healthcare diagnostics. The model demonstrates state-of-the-art performance across multiple imaging modalities including CT scans, MRI, X-rays, and ultrasound imaging. Its clinical accuracy is now approaching radiologist-level performance. |
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| <img width="80%" src="figures/fig3.png"> |
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| Compared to the previous version, MedVisionAI shows remarkable improvements in detecting subtle anomalies. For instance, in the RadBench 2025 evaluation, the model's sensitivity for early-stage tumor detection increased from 82% to 94.3%. This improvement stems from enhanced attention mechanisms: the previous model processed images at 512x512 resolution, whereas the new version operates at 1024x1024 with multi-scale feature extraction. |
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| Beyond improved detection capabilities, this version offers reduced false positive rates and enhanced support for 3D volumetric analysis. |
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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 | Model1 | Model2 | Model1-v2 | MedVisionAI | |
| |---|---|---|---|---|---| |
| | **Imaging Modalities** | CT Scan Detection | 0.845 | 0.862 | 0.871 | 0.818 | |
| | | MRI Segmentation | 0.812 | 0.829 | 0.835 | 0.821 | |
| | | X-Ray Classification | 0.891 | 0.903 | 0.912 | 0.902 | |
| | **Detection Tasks** | Ultrasound Analysis | 0.756 | 0.771 | 0.782 | 0.767 | |
| | | Pathology Detection | 0.823 | 0.841 | 0.849 | 0.779 | |
| | | Tumor Localization | 0.778 | 0.792 | 0.801 | 0.846 | |
| | | Organ Segmentation | 0.867 | 0.882 | 0.889 | 0.865 | |
| | **Specialized Tasks** | Anomaly Detection | 0.734 | 0.756 | 0.768 | 0.753 | |
| | | Fracture Identification | 0.812 | 0.828 | 0.836 | 0.829 | |
| | | Lesion Detection | 0.789 | 0.803 | 0.812 | 0.817 | |
| | | Retinal Scan | 0.856 | 0.871 | 0.879 | 0.872 | |
| | **Advanced Analysis** | Mammography | 0.823 | 0.839 | 0.848 | 0.806 | |
| | | Dermoscopy | 0.745 | 0.762 | 0.771 | 0.744 | |
| | | Cardiac Imaging | 0.801 | 0.817 | 0.826 | 0.814 | |
| | | Brain Mapping | 0.778 | 0.794 | 0.803 | 0.769 | |
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| ### Overall Performance Summary |
| MedVisionAI demonstrates exceptional performance across all evaluated medical imaging categories, with particularly strong results in tumor detection and organ segmentation tasks. |
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| ## 3. Clinical Integration & API Platform |
| We offer HIPAA-compliant API endpoints and clinical integration tools. Please contact our medical partnerships team for deployment options. |
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| ## 4. How to Run Locally |
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| Please refer to our clinical deployment guide for detailed instructions on running MedVisionAI in your medical facility. |
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| Important considerations for medical deployment: |
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| 1. FDA clearance status must be verified for your intended use case. |
| 2. All patient data must be handled according to HIPAA regulations. |
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| The model architecture of MedVisionAI is based on Vision Transformer (ViT) with medical imaging-specific pretraining. It supports both 2D and 3D input modalities. |
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| ### Input Specifications |
| The model accepts DICOM format or standard imaging formats: |
| ``` |
| Supported formats: DICOM, NIfTI, PNG, JPEG |
| Recommended resolution: 1024x1024 for 2D, 256x256x256 for 3D |
| Color space: Grayscale (1 channel) or RGB (3 channels) |
| ``` |
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| ### Inference Configuration |
| For optimal diagnostic accuracy, we recommend: |
| ``` |
| confidence_threshold = 0.75 |
| enable_uncertainty_estimation = True |
| output_format = "DICOM-SR" # Structured Report format |
| ``` |
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| ### Batch Processing |
| For high-volume diagnostic workflows: |
| ``` |
| batch_processing_template = \ |
| """[study_id]: {study_id} |
| [modality]: {modality} |
| [body_region]: {body_region} |
| [image_data_path]: {image_path} |
| [clinical_history]: {history}""" |
| ``` |
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| ## 5. License |
| This model is licensed under the [Apache 2.0 License](LICENSE). Clinical deployment requires additional certification. The model supports research and clinical use with appropriate validation. |
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| ## 6. Contact |
| For clinical partnerships and support, please contact medical-support@medvisionai.health or raise an issue on our clinical support portal. |
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