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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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# 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 imaging AI. This latest version incorporates advanced convolutional attention mechanisms and multi-scale feature fusion for unprecedented accuracy in diagnostic imaging tasks. The model has been trained on over 2 million anonymized medical images across multiple modalities including CT, MRI, X-ray, and ultrasound. |
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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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Compared to the previous version, MedVisionNet v3 shows remarkable improvements in detecting subtle abnormalities. For instance, in the RSNA 2024 pneumonia detection challenge, the model's sensitivity increased from 85% to 94.2%. This advancement stems from the hierarchical attention mechanism that allows the model to focus on clinically relevant regions. |
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Beyond its improved detection capabilities, this version also offers better explainability through attention maps and reduced false positive rates across all imaging modalities. |
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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 | ResNet-Medical | EfficientMed | DenseNet-Rad | MedVisionNet | |
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|---|---|---|---|---|---| |
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| **Detection Tasks** | Tumor Detection | 0.845 | 0.862 | 0.871 | 0.817 | |
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| | Lesion Classification | 0.792 | 0.811 | 0.823 | 0.769 | |
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| | Anomaly Detection | 0.768 | 0.789 | 0.795 | 0.753 | |
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| **Segmentation Tasks** | Organ Segmentation | 0.891 | 0.903 | 0.912 | 0.850 | |
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| | Tissue Analysis | 0.823 | 0.841 | 0.856 | 0.800 | |
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| | Vessel Tracking | 0.756 | 0.778 | 0.789 | 0.726 | |
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| | Brain Mapping | 0.812 | 0.834 | 0.845 | 0.780 | |
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| **Diagnostic Tasks** | Diagnostic Accuracy | 0.867 | 0.882 | 0.894 | 0.821 | |
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| | Nodule Detection | 0.801 | 0.823 | 0.835 | 0.745 | |
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| | Skin Analysis | 0.778 | 0.795 | 0.812 | 0.764 | |
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| | Retinal Screening | 0.845 | 0.867 | 0.878 | 0.770 | |
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| **Specialized Tasks** | Bone Density | 0.889 | 0.902 | 0.915 | 0.877 | |
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| | Cardiac Function | 0.834 | 0.856 | 0.867 | 0.776 | |
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| | Pathology Grading | 0.756 | 0.778 | 0.789 | 0.735 | |
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| | Image Quality | 0.912 | 0.923 | 0.934 | 0.877 | |
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</div> |
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### Overall Performance Summary |
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MedVisionNet demonstrates state-of-the-art performance across all evaluated medical imaging benchmark categories, with particularly notable results in tumor detection and organ segmentation tasks. |
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## 3. Clinical Integration & API |
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We offer a HIPAA-compliant API for integrating MedVisionNet into clinical workflows. Please contact our medical partnerships team for access. |
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## 4. How to Run Locally |
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Please refer to our clinical deployment guide for information about running MedVisionNet in a clinical environment. |
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Important usage guidelines for MedVisionNet: |
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1. Pre-processing pipeline must normalize images to [-1, 1] range. |
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2. Batch inference is supported for up to 32 images simultaneously. |
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3. GPU with minimum 16GB VRAM recommended for optimal performance. |
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### Input Requirements |
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Images should be pre-processed according to the following specifications: |
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```python |
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preprocessing_config = { |
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"resize": (512, 512), |
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"normalize": "minmax", |
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"color_space": "grayscale", # or "rgb" for dermoscopy |
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"bit_depth": 16 |
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} |
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``` |
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### Inference Configuration |
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We recommend the following inference settings: |
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```python |
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inference_config = { |
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"threshold": 0.5, |
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"use_tta": True, # Test-time augmentation |
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"ensemble_mode": "mean", |
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"output_attention_maps": True |
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} |
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``` |
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## 5. License |
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This model is licensed under the [Apache 2.0 License](LICENSE). Clinical use requires additional validation and regulatory approval. |
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## 6. Contact |
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For clinical partnerships and research collaborations, please contact medical-ai@medvisionnet.org. |
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