MedVision-XRay-TestRepo

toolevalxm/MedVision-XRay-TestRepo

MedVision-XRay-TestRepo is a image classification model from toolevalxm. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

<div align="center" <img src="figures/architecture.png" width="60%" alt="MedVision-XRay" / </div <hr

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: resnet
Access: Public
Created: Feb 4, 2026
Updated: Feb 4, 2026
SHA: 77cbd120

Model card

From the Hugging Face model README

MedVision-XRay

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

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

</div>

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.

Task: Image Classification
Library: transformers
Type: resnet
License: apache-2.0
Created: Feb 4, 2026
Updated: Feb 4, 2026