
Generating Severity-conditioned Knee Osteoarthritis X-rays Using Diffusion Neural Networks
Resources
Overview
This project presents an implementation and evaluation of diffusion neural networks for generating synthetic knee X-ray images. We explore both unconditional and conditional variants of Denoising Diffusion Probabilistic Models (DDPMs), with a focus on generating images across different osteoarthritis severity levels. Our conditional DDPM architecture enables controlled image generation based on Kellgren-Lawrence (KL) grades by incorporating severity information into the diffusion process.
Key Features
- Implementation of both unconditional and conditional DDPM models for knee X-ray generation
- Controlled generation based on Kellgren-Lawrence (KL) grades (0-4)
- High-fidelity synthetic images capturing grade-specific characteristics
- Quantitative evaluation using FID and Inception Score metrics
- Comprehensive model architecture with U-Net backbone
- Pre-trained model weights available for verification
Technical Details
Model Architecture
The model uses a U-Net architecture with:
- ResNet blocks with group normalization and SiLU activation
- Self-attention blocks for spatial attention
- Symmetric encoder-decoder paths with skip connections
- Learnable embedding layer for class conditioning
- Progressive spatial dimension reduction and channel depth increase
Results
- Unconditional Model:
- FID Score: 85.41
- Inception Score: 1.03
- Conditional Model (by KL Grade):
- Grade 0: FID 177.82, IS 1.56
- Grade 1: FID 173.35, IS 1.50
- Grade 2: FID 181.05, IS 1.52
- Grade 3: FID 182.03, IS 1.57
- Grade 4: FID 220.93, IS 1.57
Impact
This work addresses two critical needs in medical imaging:
- Augmenting training data for classification models by generating synthetic but realistic knee X-rays
- Creating a conditioned dataset to help train medical students and physicians by providing diverse examples across different severity grades
Citation
If you use this work in your research, please cite:
@techreport{anjum2025generating,
title={Generating Severity-conditioned Knee Osteoarthritis X-rays Using Diffusion Neural Networks},
author={Anjum, Khizar},
institution={Rutgers University},
year={2025},
url={https://github.com/khizar-anjum/KneeOAGen/blob/main/technical_report.pdf}
}