PosDiffAE: Position-Aware Diffusion Auto-Encoder for High-Resolution Brain Tissue Classification Incorporating...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Denoising diffusion models excel at high-fidelity image generation but lack explicit semantic representation extraction.
• Auto-encoders provide image-specific latent space mapping, crucial for structured data representation.
• Integrating diffusion models with auto-encoders offers a path to learn meaningful representations and organize latent spaces.

Purpose of the Study:

• To develop an auto-encoding diffusion model (PosDiffAE) for structured latent space representation in brain imaging.
• To enable unsupervised region-specific cellular pattern recognition and artifact restoration.
• To validate the model's multi-tasking capability, robustness, and generalization.

Main Methods:

• Devised a mechanism to structure the latent space by enforcing positional information regression for brain image patches.
• Developed an unsupervised tear artifact restoration technique using latent representations and diffusion model inference.
• Implemented an unsupervised JPEG artifact restoration technique guided by representations and diffusion model's noising/denoising capabilities.

Main Results:

• The model's representations demonstrated multi-tasking capabilities, including region classification, position regression, and artifact restoration.
• PosDiffAE exhibited robustness under artifact conditions.
• The model generalized effectively across fetal and adult human brain images.

Conclusions:

• PosDiffAE successfully integrates auto-encoding principles with diffusion models to create effective image representations.
• The learned representations support multiple unsupervised downstream tasks, including classification, regression, and restoration.
• The model's performance highlights the potential of structured latent spaces in diffusion models for medical image analysis.