Medical Imaging Diffusion Models

This notebook demonstrates diffusion models on realistic medical images, bridging the gap between toy examples (Swiss roll) and high-dimensional applications (gene expression).

Learning Objectives

1. Apply U-Net architecture to real medical images
2. Handle grayscale medical imaging data (X-rays, CT slices)
3. Implement data preprocessing for medical images
4. Generate synthetic medical images with diffusion models
5. Evaluate quality with domain-specific metrics

Datasets Used

We use publicly available, realistic medical imaging datasets that are computationally feasible:

1. Chest X-Ray Images (Primary Dataset)

• Source: NIH Chest X-ray Dataset (downsampled)
• Size: 128×128 grayscale images (downsampled from 1024×1024)
• Modality: X-ray (radiography)
• Use case: Generate synthetic chest X-rays
• Why: Widely used, clinically relevant, single-channel (memory efficient)
• Download: Available via Kaggle or NIH Clinical Center

2. Brain MRI Slices (Alternative)

• Source: BraTS or IXI Dataset (2D slices)
• Size: 128×128 or 256×256 grayscale
• Modality: MRI (T1, T2, FLAIR)
• Use case: Generate brain MRI slices
• Why: Important for neuroimaging, good for demonstrating multi-modal generation

3. Histopathology Patches (Advanced)

• Source: Camelyon16/17 or PatchCamelyon
• Size: 96×96 RGB patches
• Modality: H&E stained tissue
• Use case: Generate tissue patches for data augmentation
• Why: Connects to your pathology-ai-lab project

Computational Requirements

Memory-Efficient Setup (Recommended)

• Image size: 128×128 (or 64×64 for faster iteration)
• Batch size: 16-32
• Model: UNet2D with base_channels=32 or 64
• Training time: 2-4 hours on M1/M2 Mac or consumer GPU
• Memory: ~4-8GB GPU/unified memory

Full-Resolution Setup (If resources available)

• Image size: 256×256
• Batch size: 8-16
• Model: UNet2D with base_channels=64
• Training time: 8-12 hours
• Memory: ~16GB GPU memory

Notebook Structure

1. Setup & Data Loading
2. Download and preprocess medical images
3. Create PyTorch dataset

4. Visualize samples

5. Model Architecture

6. Implement UNet2D for medical images
7. Time conditioning

8. GroupNorm for small batches

9. Training

10. VP-SDE with cosine schedule
11. Score matching loss

12. Training loop with checkpointing

13. Generation & Evaluation

14. Sample synthetic images
15. Visual quality assessment
16. Quantitative metrics (FID, IS)

17. Domain-specific evaluation

18. Applications

19. Data augmentation for downstream tasks
20. Conditional generation (by disease, view angle)
21. Inpainting and super-resolution

Key Differences from Toy Examples

Aspect	Toy (Swiss Roll)	Medical Imaging
Data	2D points	128×128 images (16K dims)
Architecture	Simple MLP	U-Net with skip connections
Training time	Minutes	Hours
Evaluation	Visual	FID, clinical metrics
Applications	Educational	Data augmentation, synthesis

Prerequisites

• Completed 02_sde_formulation.ipynb
• Understanding of convolutional neural networks
• Familiarity with medical imaging (helpful but not required)

Next Steps

After this notebook: - 04_gene_expression_diffusion.ipynb: High-dimensional tabular data - Your pathology-ai-lab project: Whole-slide imaging with diffusion models

References

Datasets

• NIH Chest X-ray: Wang et al. (2017) "ChestX-ray8: Hospital-scale Chest X-ray Database"
• BraTS: Menze et al. (2015) "The Multimodal Brain Tumor Image Segmentation Benchmark"
• Camelyon: Bejnordi et al. (2017) "Diagnostic Assessment of Deep Learning Algorithms"

Medical Imaging Diffusion

• MedSegDiff: Wu et al. (2023) "MedSegDiff: Medical Image Segmentation with Diffusion Models"
• DiffMIC: Özbey et al. (2023) "Unsupervised Medical Image Translation with Adversarial Diffusion Models"
• RoentGen: Chambon et al. (2022) "RoentGen: Vision-Language Foundation Model for Chest X-ray Generation"

Architecture

• U-Net: Ronneberger et al. (2015) "U-Net: Convolutional Networks for Biomedical Image Segmentation"
• DDPM: Ho et al. (2020) "Denoising Diffusion Probabilistic Models"