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--- |
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license: apache-2.0 |
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library_name: timm |
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--- |
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# MedVisionNet |
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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="MedVisionNet" /> |
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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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MedVisionNet represents a breakthrough in medical image analysis, specifically designed for multi-class pathology detection in chest X-rays. This model leverages state-of-the-art convolutional neural network architectures combined with attention mechanisms to achieve exceptional diagnostic accuracy. |
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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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The model has been trained on over 200,000 annotated chest X-ray images from multiple healthcare institutions worldwide. It can detect 14 different pathological conditions including pneumonia, cardiomegaly, pleural effusion, and nodules with high sensitivity and specificity. |
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In clinical validation studies, MedVisionNet achieved a mean AUC-ROC of 0.94 across all pathology classes, outperforming both radiologist baselines and previous state-of-the-art models by a significant margin. |
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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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| | Metric | ResNet50 | DenseNet121 | EfficientNet-B4 | MedVisionNet | |
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|---|---|---|---|---|---| |
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| **Classification Metrics** | Sensitivity | 0.821 | 0.835 | 0.842 | 0.779 | |
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| | Specificity | 0.889 | 0.901 | 0.910 | 0.875 | |
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| | AUC-ROC | 0.915 | 0.928 | 0.935 | 0.858 | |
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| **Detection Metrics** | Precision | 0.782 | 0.799 | 0.811 | 0.749 | |
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| | Recall | 0.765 | 0.781 | 0.790 | 0.767 | |
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| | F1-Score | 0.773 | 0.790 | 0.800 | 0.751 | |
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| | Dice Coefficient | 0.712 | 0.731 | 0.745 | 0.600 | |
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| **Segmentation Metrics** | IoU Score | 0.668 | 0.689 | 0.701 | 0.633 | |
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| | Hausdorff Distance | 12.5 | 11.2 | 10.1 | 0.725 | |
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| | Boundary Accuracy | 0.745 | 0.762 | 0.778 | 0.701 | |
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| | Volumetric Overlap | 0.701 | 0.722 | 0.738 | 0.766 | |
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| **Reliability Metrics** | Calibration Error | 0.089 | 0.078 | 0.065 | 0.769 | |
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| | Lesion Detection | 0.812 | 0.829 | 0.841 | 0.836 | |
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| | Inference Speed | 45.2 | 38.1 | 32.5 | 0.769 | |
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| | Robustness Test | 0.756 | 0.771 | 0.789 | 0.732 | |
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</div> |
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### Overall Performance Summary |
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MedVisionNet demonstrates exceptional performance across all evaluated medical imaging metrics, with particularly notable results in sensitivity and AUC-ROC for pathology detection. |
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## 3. Clinical Integration & API Platform |
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We provide HIPAA-compliant API endpoints for clinical integration. Contact our medical AI team for deployment options and regulatory compliance documentation. |
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## 4. How to Run Locally |
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Please refer to our code repository for information about running MedVisionNet locally in a clinical environment. |
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Key requirements: |
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1. CUDA-enabled GPU with minimum 8GB VRAM |
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2. DICOM image preprocessing pipeline |
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3. Patient data anonymization module |
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### Model Architecture |
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MedVisionNet uses a modified ResNet-152 backbone with multi-scale feature pyramid network and class-activation mapping for interpretability. |
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### Input Specifications |
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``` |
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Image Format: DICOM or PNG (grayscale) |
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Resolution: 512x512 pixels (automatically resized) |
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Normalization: ImageNet statistics |
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``` |
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### Inference Example |
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```python |
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from medvisionnet import MedVisionNet, preprocess_xray |
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model = MedVisionNet.from_pretrained("medvision/MedVisionNet-ChestXray") |
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image = preprocess_xray("path/to/xray.dcm") |
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predictions = model.predict(image) |
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for pathology, confidence in predictions.items(): |
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print(f"{pathology}: {confidence:.3f}") |
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``` |
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### Clinical Thresholds |
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For clinical deployment, we recommend the following confidence thresholds: |
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- **High confidence**: > 0.85 |
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- **Review required**: 0.50 - 0.85 |
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- **Likely negative**: < 0.50 |
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## 5. License |
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This model is licensed under the [Apache 2.0 License](LICENSE). Use in clinical settings requires additional regulatory approval and is subject to local medical device regulations. |
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## 6. Contact |
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For clinical partnerships and regulatory inquiries, contact us at clinical@medvisionnet.ai |
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For research collaborations, reach out to research@medvisionnet.ai |
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