---
license: apache-2.0
library_name: transformers
---
# MedVision-DiagnosticsAI
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  <img src="figures/architecture.png" width="60%" alt="MedVision-DiagnosticsAI" />
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  <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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## 1. Introduction

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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  <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.

Key features of MedVision-DiagnosticsAI:
- Multi-modal medical image classification
- High sensitivity for early disease detection
- Calibrated uncertainty estimates
- HIPAA-compliant deployment options

## 2. Evaluation Results

### Comprehensive Benchmark Results

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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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### Overall Performance Summary
MedVision-DiagnosticsAI demonstrates exceptional performance across all evaluated clinical benchmarks, with particularly strong results in sensitivity and multi-modal classification tasks.

## 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

## 4. How to Run Locally

Please refer to our code repository for detailed instructions on running MedVision-DiagnosticsAI locally.

### System Requirements
- GPU with at least 8GB VRAM
- Python 3.8+
- transformers >= 4.30.0

### Quick Start
```python
from transformers import AutoModelForImageClassification, AutoImageProcessor

model = AutoModelForImageClassification.from_pretrained("your-org/MedVision-DiagnosticsAI")
processor = AutoImageProcessor.from_pretrained("your-org/MedVision-DiagnosticsAI")

# Process your medical image
inputs = processor(images=your_image, return_tensors="pt")
outputs = model(**inputs)
```

### Inference Parameters
We recommend the following settings for optimal performance:
- Batch size: 1 (for clinical applications)
- Image size: 224x224
- Normalization: ImageNet statistics

## 5. License
This model is licensed under the [Apache 2.0 License](LICENSE). The model is intended for research and clinical decision support only.

## 6. Contact
If you have any questions, please raise an issue on our GitHub repository or contact us at support@medvision-ai.org.

## 7. Citation
```bibtex
@article{medvision2025,
  title={MedVision-DiagnosticsAI: A Multi-Modal Medical Imaging Foundation Model},
  author={MedVision Team},
  journal={arXiv preprint},
  year={2025}
}
```