toolevalxm/MedVision-XRay-TestRepo

MedVision-XRay

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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.

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

	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

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:

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. 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.