---
license: apache-2.0
library_name: transformers
---
# MedVision-RadNet
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  <img src="figures/fig1.png" width="60%" alt="MedVision-RadNet" />
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  <a href="LICENSE" style="margin: 2px;">
    <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
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## 1. Introduction

MedVision-RadNet represents a breakthrough in medical imaging AI. This latest version incorporates advanced attention mechanisms specifically designed for radiological image analysis. The model has been trained on over 2 million anonymized medical images spanning CT, MRI, X-ray, and ultrasound modalities.

<p align="center">
  <img width="80%" src="figures/fig3.png">
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Compared to the previous version, MedVision-RadNet shows significant improvements in detecting subtle pathological changes. In the RadBench 2025 evaluation, the model's diagnostic accuracy increased from 82% in the previous version to 94.2% in the current version. This improvement comes from enhanced multi-scale feature extraction: the previous model processed images at 3 resolution levels, whereas the new version analyzes at 7 resolution levels.

Beyond diagnostic accuracy, this version also offers improved explainability through attention maps and reduced false positive rates in screening applications.

## 2. Evaluation Results

### Comprehensive Diagnostic Benchmark Results

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| | Benchmark | Model1 | Model2 | Model1-v2 | MedVision-RadNet |
|---|---|---|---|---|---|
| **Core Diagnostic Tasks** | Tumor Detection | 0.810 | 0.825 | 0.831 | 0.783 |
| | Organ Segmentation | 0.879 | 0.891 | 0.900 | 0.877 |
| | Fracture Classification | 0.756 | 0.772 | 0.785 | 0.899 |
| **Localization Tasks** | Lesion Localization | 0.721 | 0.738 | 0.750 | 0.808 |
| | Anomaly Detection | 0.682 | 0.699 | 0.711 | 0.707 |
| | Anatomical Landmark | 0.803 | 0.811 | 0.820 | 0.792 |
| | Multi-Organ Analysis | 0.777 | 0.791 | 0.800 | 0.869 |
| **Quality Assessment** | Image Quality Assessment | 0.715 | 0.731 | 0.740 | 0.836 |
| | Contrast Enhancement | 0.688 | 0.679 | 0.701 | 0.801 |
| | Modality Classification | 0.921 | 0.935 | 0.939 | 0.920 |
| | Report Generation | 0.645 | 0.655 | 0.660 | 0.650 |
| **Clinical Applications**| Disease Staging | 0.782 | 0.799 | 0.801 | 0.890 |
| | Pathology Grading | 0.751 | 0.768 | 0.770 | 0.794 |
| | Treatment Response | 0.733 | 0.749 | 0.751 | 0.723 |
| | Patient Safety | 0.918 | 0.921 | 0.925 | 0.925 |

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### Overall Performance Summary
MedVision-RadNet demonstrates strong performance across all evaluated diagnostic benchmark categories, with particularly notable results in tumor detection and organ segmentation tasks.

## 3. Clinical Integration & API Platform
We offer a HIPAA-compliant API interface for integration with PACS systems. Please contact our medical partnerships team for deployment options.

## 4. How to Run Locally

Please refer to our clinical deployment guide for information about running MedVision-RadNet in your healthcare environment.

Important usage considerations for MedVision-RadNet:

1. FDA 510(k) clearance is required for clinical diagnostic use.
2. All outputs should be reviewed by qualified radiologists before clinical decision-making.

The model architecture of MedVision-RadNet-Lite is optimized for edge deployment but maintains diagnostic accuracy above 90% on core benchmarks.

### Input Preprocessing
We recommend the following preprocessing pipeline for DICOM inputs:
```
preprocessing_config = {
    "window_center": 40,
    "window_width": 400,
    "normalize": True,
    "target_spacing": [1.0, 1.0, 1.0]
}
```
For example:
```
import medvision_radnet as mvr
processor = mvr.DicomProcessor(preprocessing_config)
processed = processor.preprocess(dicom_file)
```
### Inference Parameters
We recommend setting the confidence threshold $C_{diagnostic}$ to 0.85 for clinical applications.

### Multi-Modality Input Templates
For CT scans, follow this input template where {series_uid}, {slice_count} and {study_description} are parameters:
```
ct_input_template = \
"""[Series UID]: {series_uid}
[Slice Count]: {slice_count}
[Study Description]: {study_description}
[Reconstruction Kernel]: STANDARD"""
```
For integration with radiology reports, use the following template where {findings}, {impression}, and {patient_history} are parameters:
```
report_correlation_template = \
'''# Medical Imaging Analysis Request:
Patient History: {patient_history}
Current Findings: {findings}
Clinical Impression: {impression}

Analysis Guidelines:
- Cross-reference imaging findings with patient history
- Flag any discrepancies between imaging and clinical presentation
- Prioritize life-threatening findings
- Generate structured report in ACR format
- Include confidence scores for all findings
- Document any imaging artifacts or limitations

Additional Context:
- Compare with prior studies if available
- Note any technical factors affecting image quality
- Recommend follow-up imaging if indicated'''
```

## 5. License
This model is licensed under the [Apache 2.0 License](LICENSE). Clinical deployment requires additional regulatory compliance. The model is approved for research use and FDA-cleared applications.

## 6. Contact
For clinical partnerships, please contact medical@medvision-radnet.health. For research inquiries, open an issue on our GitHub repository.