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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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# MediScanPro |
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<div align="center"> |
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<img src="figures/fig1.png" width="60%" alt="MediScanPro" /> |
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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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MediScanPro represents a breakthrough in medical image analysis. This advanced vision model has been specifically trained on diverse medical imaging datasets including X-rays, CT scans, MRIs, and pathology slides. By leveraging transformer-based architectures combined with domain-specific pre-training, MediScanPro achieves state-of-the-art performance across multiple medical imaging benchmarks. |
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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 incorporates several innovations including multi-scale feature extraction, attention-guided region highlighting, and uncertainty quantification for clinical decision support. In clinical validation studies, MediScanPro demonstrated 94% sensitivity in detecting critical findings with a specificity of 91%. |
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MediScanPro is designed to assist radiologists and healthcare professionals in their diagnostic workflows, potentially reducing analysis time by 60% while maintaining high accuracy standards. |
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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-152 | EfficientNet-B7 | ViT-L | MediScanPro | |
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|---|---|---|---|---|---| |
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| **Detection Tasks** | X-ray Detection | 0.821 | 0.845 | 0.867 | 0.827 | |
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| | CT Segmentation | 0.756 | 0.782 | 0.801 | 0.806 | |
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| | MRI Classification | 0.698 | 0.721 | 0.745 | 0.764 | |
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| **Specialized Imaging** | Pathology Analysis | 0.612 | 0.645 | 0.678 | 0.694 | |
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| | Ultrasound Detection | 0.589 | 0.612 | 0.641 | 0.655 | |
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| | Fundus Screening | 0.734 | 0.761 | 0.789 | 0.805 | |
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| | Dermoscopy Classification | 0.667 | 0.698 | 0.721 | 0.750 | |
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| **Organ-Specific** | Mammography Detection | 0.812 | 0.834 | 0.856 | 0.810 | |
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| | Cardiac Assessment | 0.701 | 0.729 | 0.754 | 0.750 | |
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| | Brain Tumor Detection | 0.778 | 0.801 | 0.823 | 0.842 | |
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| | Lung Nodule Detection | 0.745 | 0.773 | 0.798 | 0.806 | |
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| **Advanced Tasks** | Bone Fracture Detection | 0.834 | 0.857 | 0.878 | 0.840 | |
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| | Tissue Classification | 0.689 | 0.715 | 0.742 | 0.740 | |
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| | Anomaly Detection | 0.623 | 0.651 | 0.679 | 0.670 | |
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| | Multi-organ Segmentation | 0.567 | 0.598 | 0.631 | 0.605 | |
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</div> |
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### Overall Performance Summary |
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MediScanPro demonstrates exceptional performance across all evaluated medical imaging benchmark categories, with particularly strong results in detection and organ-specific analysis tasks. |
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## 3. API & Clinical Integration |
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We provide REST APIs and DICOM integration modules for seamless deployment in clinical environments. Contact our enterprise team for integration support. |
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## 4. How to Run Locally |
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Please refer to our clinical deployment guide for information about running MediScanPro in your healthcare facility. |
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Key deployment considerations: |
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1. HIPAA compliance modules are included by default. |
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2. GPU acceleration is recommended for real-time analysis. |
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### Recommended Configuration |
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We recommend the following system configuration: |
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``` |
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GPU: NVIDIA A100 or equivalent (16GB+ VRAM) |
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RAM: 64GB minimum |
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Storage: SSD with 500GB+ for model and cache |
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``` |
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### Input Preprocessing |
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For medical images, please follow this preprocessing template: |
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```python |
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preprocessing_config = { |
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"target_size": (512, 512), |
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"normalize": True, |
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"window_level": "auto", # For CT/MRI |
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"color_mode": "grayscale", # or "rgb" for dermoscopy |
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} |
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``` |
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## 5. License |
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This code repository is licensed under the [Apache 2.0 License](LICENSE). The use of MediScanPro models requires compliance with medical device regulations in your jurisdiction. |
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## 6. Contact |
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For clinical inquiries, please contact medical@mediscanpro.ai. For technical support, raise an issue on our GitHub repository. |
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``` |
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