MedVision-DiagNet-TestRepo

MedVision-DiagNet

<!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="figures/architecture.png" width="60%" alt="MedVision-DiagNet" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="figures/license_badge.png" style="display: inline-block; vertical-align: middle;"/> </a> </div>

1. Introduction

MedVision-DiagNet is a state-of-the-art Vision Transformer (ViT) model specifically designed for medical imaging analysis and diagnosis. The model has been trained on a diverse collection of medical imaging datasets including X-rays, CT scans, MRI images, and pathology slides.

<p align="center"> <img width="80%" src="figures/performance_chart.png"> </p>

MedVision-DiagNet demonstrates exceptional capabilities across multiple medical imaging modalities. The model achieves competitive performance with radiologist-level accuracy on several benchmark tasks, particularly in tumor detection and lung nodule identification.

Key improvements in this version include:

• Enhanced feature extraction for small lesion detection
• Improved generalization across different imaging equipment
• Reduced false positive rates while maintaining high sensitivity

2. Evaluation Results

Comprehensive Benchmark Results

<div align="center"> </div>

Overall Performance Summary

MedVision-DiagNet demonstrates exceptional performance across all medical imaging benchmarks, with particular strength in oncology and pulmonary imaging tasks. The model achieves state-of-the-art results on tumor detection and lung nodule identification.

3. Clinical Applications

This model is intended for research purposes and clinical decision support. It should not be used as a standalone diagnostic tool. Always consult qualified healthcare professionals for medical diagnoses.

4. How to Run Locally

Please refer to our code repository for more information about running MedVision-DiagNet locally.

Model Loading

from transformers import ViTForImageClassification, ViTImageProcessor

model = ViTForImageClassification.from_pretrained("username/MedVision-DiagNet")
processor = ViTImageProcessor.from_pretrained("username/MedVision-DiagNet")

Inference

from PIL import Image

image = Image.open("medical_scan.png")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)

Preprocessing Recommendations

For optimal performance:

• Input resolution: 224x224 or 384x384
• Normalization: ImageNet standards (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
• DICOM images should be converted to PNG/JPEG with appropriate windowing

5. License

This model is licensed under the Apache 2.0 License.

6. Contact

For questions or collaborations, please contact us at [email protected] or open an issue on our GitHub repository.

7. Citation

@article{medvision2025,
  title={MedVision-DiagNet: A Vision Transformer for Multi-Modal Medical Imaging},
  author={MedVision AI Research Team},
  journal={Nature Medicine},
  year={2025}
}