---
license: apache-2.0
library_name: transformers
---
# MedVision-XRay

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  <img src="figures/architecture.png" width="60%" alt="MedVision-XRay" />
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  <a href="LICENSE" style="margin: 2px;">
    <img alt="License" src="figures/badge.png" style="display: inline-block; vertical-align: middle;"/>
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## 1. Introduction

MedVision-XRay is a state-of-the-art deep learning model for multi-label chest X-ray classification. The model has been trained on large-scale medical imaging datasets and can detect 14 different thoracic pathologies including pneumonia, atelectasis, cardiomegaly, and more.

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  <img width="80%" src="figures/performance.png">
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The model architecture is based on a modified ResNet backbone with attention mechanisms specifically designed for medical imaging applications. It achieves competitive performance across multiple medical imaging benchmarks.

## 2. Evaluation Results

### Comprehensive Benchmark Results

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| | Benchmark | BaselineModel | ResNet-50 | DenseNet-121 | MedVision-XRay |
|---|---|---|---|---|---|
| **Detection Tasks** | Pneumonia Detection | 0.821 | 0.845 | 0.856 | 0.840 |
| | Atelectasis Detection | 0.756 | 0.778 | 0.785 | 0.809 |
| | Cardiomegaly Detection | 0.889 | 0.901 | 0.912 | 0.919 |
| **Segmentation Tasks** | Lung Segmentation | 0.912 | 0.925 | 0.931 | 0.934 |
| | Lesion Segmentation | 0.782 | 0.801 | 0.815 | 0.811 |
| | Cardiac Segmentation | 0.845 | 0.862 | 0.871 | 0.861 |
| **Classification Tasks** | Multi-label Classification | 0.723 | 0.751 | 0.768 | 0.775 |
| | Severity Grading | 0.678 | 0.701 | 0.712 | 0.761 |
| | Finding Localization | 0.645 | 0.678 | 0.692 | 0.688 |
| **Robustness Tests** | Noise Resistance | 0.812 | 0.834 | 0.845 | 0.847 |
| | Contrast Variation | 0.789 | 0.812 | 0.825 | 0.815 |
| | Resolution Invariance | 0.756 | 0.778 | 0.789 | 0.800 |

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### Overall Performance Summary
MedVision-XRay demonstrates strong performance across all evaluated medical imaging benchmark categories, with particularly notable results in detection and segmentation tasks.

## 3. Model Usage

The model can be loaded using the Hugging Face Transformers library:

```python
from transformers import AutoModelForImageClassification, AutoFeatureExtractor

model = AutoModelForImageClassification.from_pretrained("medical-ai/MedVision-XRay")
feature_extractor = AutoFeatureExtractor.from_pretrained("medical-ai/MedVision-XRay")
```

## 4. Training Details

The model was trained with the following configuration:
- Optimizer: AdamW with learning rate 1e-4
- Batch size: 32
- Image resolution: 224x224
- Augmentations: Random rotation, horizontal flip, color jitter
- Training epochs: 50

## 5. License
This model is licensed under the [Apache 2.0 License](LICENSE). For research and clinical use, please consult with your institution's IRB.

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