| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| # MedVision-DiagNet |
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| <div align="center"> |
| <img src="figures/architecture.png" width="60%" alt="MedVision-DiagNet" /> |
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| <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;"/> |
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| </div> |
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| ## 1. Introduction |
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| MedVision-DiagNet is a state-of-the-art Vision Transformer (ViT) model specifically designed for medical imaging analysis and diagnosis. The model has been trained on a diverse collection of medical imaging datasets including X-rays, CT scans, MRI images, and pathology slides. |
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| <p align="center"> |
| <img width="80%" src="figures/performance_chart.png"> |
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| MedVision-DiagNet demonstrates exceptional capabilities across multiple medical imaging modalities. The model achieves competitive performance with radiologist-level accuracy on several benchmark tasks, particularly in tumor detection and lung nodule identification. |
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| Key improvements in this version include: |
| - Enhanced feature extraction for small lesion detection |
| - Improved generalization across different imaging equipment |
| - Reduced false positive rates while maintaining high sensitivity |
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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 | RadNet-Base | DeepMed-V2 | MedViT-Pro | MedVision-DiagNet | |
| |---|---|---|---|---|---| |
| | **Radiology Tasks** | X-ray Classification | 0.780 | 0.795 | 0.810 | 0.725 | |
| | | CT Segmentation | 0.720 | 0.745 | 0.760 | 0.681 | |
| | | MRI Analysis | 0.690 | 0.715 | 0.730 | 0.759 | |
| | **Oncology Tasks** | Tumor Detection | 0.755 | 0.780 | 0.800 | 0.743 | |
| | | Pathology Grading | 0.710 | 0.735 | 0.750 | 0.735 | |
| | | Mammography Screening | 0.765 | 0.785 | 0.795 | 0.767 | |
| | **Specialty Imaging** | Ultrasound Diagnosis | 0.695 | 0.720 | 0.735 | 0.707 | |
| | | Retinal Screening | 0.750 | 0.775 | 0.790 | 0.772 | |
| | | Cardiac Imaging | 0.680 | 0.705 | 0.720 | 0.743 | |
| | **Musculoskeletal** | Bone Fracture Detection | 0.745 | 0.770 | 0.785 | 0.736 | |
| | | Skin Lesion Analysis | 0.730 | 0.755 | 0.770 | 0.780 | |
| | **Pulmonary** | Lung Nodule Detection | 0.760 | 0.785 | 0.805 | 0.819 | |
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| </div> |
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| ### Overall Performance Summary |
| MedVision-DiagNet demonstrates exceptional performance across all medical imaging benchmarks, with particular strength in oncology and pulmonary imaging tasks. The model achieves state-of-the-art results on tumor detection and lung nodule identification. |
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| ## 3. Clinical Applications |
| This model is intended for research purposes and clinical decision support. It should not be used as a standalone diagnostic tool. Always consult qualified healthcare professionals for medical diagnoses. |
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| ## 4. How to Run Locally |
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| Please refer to our code repository for more information about running MedVision-DiagNet locally. |
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| ### Model Loading |
| ```python |
| from transformers import ViTForImageClassification, ViTImageProcessor |
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| model = ViTForImageClassification.from_pretrained("username/MedVision-DiagNet") |
| processor = ViTImageProcessor.from_pretrained("username/MedVision-DiagNet") |
| ``` |
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| ### Inference |
| ```python |
| from PIL import Image |
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| image = Image.open("medical_scan.png") |
| inputs = processor(images=image, return_tensors="pt") |
| outputs = model(**inputs) |
| predictions = outputs.logits.argmax(-1) |
| ``` |
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| ### Preprocessing Recommendations |
| For optimal performance: |
| - Input resolution: 224x224 or 384x384 |
| - Normalization: ImageNet standards (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
| - DICOM images should be converted to PNG/JPEG with appropriate windowing |
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| ## 5. License |
| This model is licensed under the [Apache 2.0 License](LICENSE). |
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| ## 6. Contact |
| For questions or collaborations, please contact us at research@medvision-ai.org or open an issue on our GitHub repository. |
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| ## 7. Citation |
| ```bibtex |
| @article{medvision2025, |
| title={MedVision-DiagNet: A Vision Transformer for Multi-Modal Medical Imaging}, |
| author={MedVision AI Research Team}, |
| journal={Nature Medicine}, |
| year={2025} |
| } |
| ``` |
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