| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| # MedVision-DiagnosticsAI |
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| <div align="center"> |
| <img src="figures/architecture.png" width="60%" alt="MedVision-DiagnosticsAI" /> |
| </div> |
| <hr> |
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| <div align="center" style="line-height: 1;"> |
| <a href="LICENSE" style="margin: 2px;"> |
| <img alt="License" src="figures/license_badge.png" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
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| ## 1. Introduction |
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| MedVision-DiagnosticsAI represents a breakthrough in medical imaging analysis, leveraging state-of-the-art Vision Transformer (ViT) architecture for multi-modal diagnostic tasks. The model has been extensively fine-tuned on diverse medical imaging datasets including X-rays, CT scans, and MRI images. |
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| <p align="center"> |
| <img width="80%" src="figures/performance_chart.png"> |
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| Our model achieves remarkable performance on several clinical benchmarks, demonstrating its potential for assisting healthcare professionals in diagnostic workflows. The architecture combines attention mechanisms with domain-specific pre-training to capture subtle patterns in medical imagery. |
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| Key features of MedVision-DiagnosticsAI: |
| - Multi-modal medical image classification |
| - High sensitivity for early disease detection |
| - Calibrated uncertainty estimates |
| - HIPAA-compliant deployment options |
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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 | Baseline | ModelA | ModelB-v2 | MedVision-DiagnosticsAI | |
| |---|---|---|---|---|---| |
| | **Classification Tasks** | Chest X-Ray Classification | 0.821 | 0.845 | 0.867 | 0.892 | |
| | | CT Scan Analysis | 0.756 | 0.778 | 0.801 | 0.844 | |
| | | MRI Segmentation | 0.698 | 0.721 | 0.745 | 0.856 | |
| | **Detection Tasks** | Tumor Detection | 0.812 | 0.834 | 0.851 | 0.889 | |
| | | Anomaly Localization | 0.745 | 0.768 | 0.789 | 0.819 | |
| | | Lesion Identification | 0.789 | 0.812 | 0.835 | 0.896 | |
| | **Clinical Metrics** | Sensitivity | 0.867 | 0.889 | 0.901 | 0.932 | |
| | | Specificity | 0.834 | 0.856 | 0.878 | 0.894 | |
| | | PPV (Precision) | 0.812 | 0.834 | 0.856 | 0.877 | |
| | | NPV | 0.845 | 0.867 | 0.889 | 0.914 | |
| | **Robustness** | Cross-Domain Transfer | 0.678 | 0.701 | 0.723 | 0.787 | |
| | | Noise Resilience | 0.712 | 0.734 | 0.756 | 0.797 | |
| | | Calibration Error | 0.089 | 0.078 | 0.067 | 0.065 | |
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| </div> |
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| ### Overall Performance Summary |
| MedVision-DiagnosticsAI demonstrates exceptional performance across all evaluated clinical benchmarks, with particularly strong results in sensitivity and multi-modal classification tasks. |
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| ## 3. Clinical Applications |
| Our model is designed to assist healthcare professionals in: |
| - Rapid screening of chest X-rays |
| - CT scan abnormality detection |
| - MRI-based tissue analysis |
| - Cross-modality diagnostic support |
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| ## 4. How to Run Locally |
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| Please refer to our code repository for detailed instructions on running MedVision-DiagnosticsAI locally. |
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| ### System Requirements |
| - GPU with at least 8GB VRAM |
| - Python 3.8+ |
| - transformers >= 4.30.0 |
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| ### Quick Start |
| ```python |
| from transformers import AutoModelForImageClassification, AutoImageProcessor |
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| model = AutoModelForImageClassification.from_pretrained("your-org/MedVision-DiagnosticsAI") |
| processor = AutoImageProcessor.from_pretrained("your-org/MedVision-DiagnosticsAI") |
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| # Process your medical image |
| inputs = processor(images=your_image, return_tensors="pt") |
| outputs = model(**inputs) |
| ``` |
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| ### Inference Parameters |
| We recommend the following settings for optimal performance: |
| - Batch size: 1 (for clinical applications) |
| - Image size: 224x224 |
| - Normalization: ImageNet statistics |
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| ## 5. License |
| This model is licensed under the [Apache 2.0 License](LICENSE). The model is intended for research and clinical decision support only. |
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| ## 6. Contact |
| If you have any questions, please raise an issue on our GitHub repository or contact us at support@medvision-ai.org. |
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| ## 7. Citation |
| ```bibtex |
| @article{medvision2025, |
| title={MedVision-DiagnosticsAI: A Multi-Modal Medical Imaging Foundation Model}, |
| author={MedVision Team}, |
| journal={arXiv preprint}, |
| year={2025} |
| } |
| ``` |
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