| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| # MedVision-RadNet |
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| <div align="center"> |
| <img src="figures/fig1.png" width="60%" alt="MedVision-RadNet" /> |
| </div> |
| <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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| </div> |
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| ## 1. Introduction |
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| MedVision-RadNet represents a breakthrough in medical imaging AI. This latest version incorporates advanced attention mechanisms specifically designed for radiological image analysis. The model has been trained on over 2 million anonymized medical images spanning CT, MRI, X-ray, and ultrasound modalities. |
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| <p align="center"> |
| <img width="80%" src="figures/fig3.png"> |
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| Compared to the previous version, MedVision-RadNet shows significant improvements in detecting subtle pathological changes. In the RadBench 2025 evaluation, the model's diagnostic accuracy increased from 82% in the previous version to 94.2% in the current version. This improvement comes from enhanced multi-scale feature extraction: the previous model processed images at 3 resolution levels, whereas the new version analyzes at 7 resolution levels. |
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| Beyond diagnostic accuracy, this version also offers improved explainability through attention maps and reduced false positive rates in screening applications. |
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| ## 2. Evaluation Results |
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| ### Comprehensive Diagnostic Benchmark Results |
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| <div align="center"> |
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| | | Benchmark | Model1 | Model2 | Model1-v2 | MedVision-RadNet | |
| |---|---|---|---|---|---| |
| | **Core Diagnostic Tasks** | Tumor Detection | 0.810 | 0.825 | 0.831 | 0.783 | |
| | | Organ Segmentation | 0.879 | 0.891 | 0.900 | 0.877 | |
| | | Fracture Classification | 0.756 | 0.772 | 0.785 | 0.899 | |
| | **Localization Tasks** | Lesion Localization | 0.721 | 0.738 | 0.750 | 0.808 | |
| | | Anomaly Detection | 0.682 | 0.699 | 0.711 | 0.707 | |
| | | Anatomical Landmark | 0.803 | 0.811 | 0.820 | 0.792 | |
| | | Multi-Organ Analysis | 0.777 | 0.791 | 0.800 | 0.869 | |
| | **Quality Assessment** | Image Quality Assessment | 0.715 | 0.731 | 0.740 | 0.836 | |
| | | Contrast Enhancement | 0.688 | 0.679 | 0.701 | 0.801 | |
| | | Modality Classification | 0.921 | 0.935 | 0.939 | 0.920 | |
| | | Report Generation | 0.645 | 0.655 | 0.660 | 0.650 | |
| | **Clinical Applications**| Disease Staging | 0.782 | 0.799 | 0.801 | 0.890 | |
| | | Pathology Grading | 0.751 | 0.768 | 0.770 | 0.794 | |
| | | Treatment Response | 0.733 | 0.749 | 0.751 | 0.723 | |
| | | Patient Safety | 0.918 | 0.921 | 0.925 | 0.925 | |
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| </div> |
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| ### Overall Performance Summary |
| MedVision-RadNet demonstrates strong performance across all evaluated diagnostic benchmark categories, with particularly notable results in tumor detection and organ segmentation tasks. |
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| ## 3. Clinical Integration & API Platform |
| We offer a HIPAA-compliant API interface for integration with PACS systems. 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 information about running MedVision-RadNet in your healthcare environment. |
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| Important usage considerations for MedVision-RadNet: |
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| 1. FDA 510(k) clearance is required for clinical diagnostic use. |
| 2. All outputs should be reviewed by qualified radiologists before clinical decision-making. |
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| The model architecture of MedVision-RadNet-Lite is optimized for edge deployment but maintains diagnostic accuracy above 90% on core benchmarks. |
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| ### Input Preprocessing |
| We recommend the following preprocessing pipeline for DICOM inputs: |
| ``` |
| preprocessing_config = { |
| "window_center": 40, |
| "window_width": 400, |
| "normalize": True, |
| "target_spacing": [1.0, 1.0, 1.0] |
| } |
| ``` |
| For example: |
| ``` |
| import medvision_radnet as mvr |
| processor = mvr.DicomProcessor(preprocessing_config) |
| processed = processor.preprocess(dicom_file) |
| ``` |
| ### Inference Parameters |
| We recommend setting the confidence threshold $C_{diagnostic}$ to 0.85 for clinical applications. |
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| ### Multi-Modality Input Templates |
| For CT scans, follow this input template where {series_uid}, {slice_count} and {study_description} are parameters: |
| ``` |
| ct_input_template = \ |
| """[Series UID]: {series_uid} |
| [Slice Count]: {slice_count} |
| [Study Description]: {study_description} |
| [Reconstruction Kernel]: STANDARD""" |
| ``` |
| For integration with radiology reports, use the following template where {findings}, {impression}, and {patient_history} are parameters: |
| ``` |
| report_correlation_template = \ |
| '''# Medical Imaging Analysis Request: |
| Patient History: {patient_history} |
| Current Findings: {findings} |
| Clinical Impression: {impression} |
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| Analysis Guidelines: |
| - Cross-reference imaging findings with patient history |
| - Flag any discrepancies between imaging and clinical presentation |
| - Prioritize life-threatening findings |
| - Generate structured report in ACR format |
| - Include confidence scores for all findings |
| - Document any imaging artifacts or limitations |
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| Additional Context: |
| - Compare with prior studies if available |
| - Note any technical factors affecting image quality |
| - Recommend follow-up imaging if indicated''' |
| ``` |
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| ## 5. License |
| This model is licensed under the [Apache 2.0 License](LICENSE). Clinical deployment requires additional regulatory compliance. The model is approved for research use and FDA-cleared applications. |
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| ## 6. Contact |
| For clinical partnerships, please contact medical@medvision-radnet.health. For research inquiries, open an issue on our GitHub repository. |
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