PosDiffAE: Position-aware Diffusion Auto-encoder For High-Resolution Brain Tissue Classification Incorporating Artifact Restoration

2025-07-03

AI Summary

This study addresses region-specific cellular pattern recognition and artifact correction in high-resolution brain images. Methodologically: (1) a location-aware latent space is designed, enforcing anatomical semantic alignment via neighborhood constraints and latent variable position regression; (2) a diffusion-augmented structured autoencoder architecture is proposed, integrating a steerable noise modulation mechanism into the denoising process to enable inference-time representation steering; (3) tear and JPEG artifact correction is achieved fully unsupervisedly. The key contribution lies in unifying latent-space geometric modeling, controllable diffusion dynamics, and pathological image restoration within a single annotation-free framework. Experimental results demonstrate significant improvements—+8.2% mIoU in tissue classification accuracy and +6.4 dB PSNR in artifact reconstruction quality—establishing a robust, interpretable latent representation foundation for digital pathology analysis.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Denoising diffusion models produce high-fidelity image samples by capturing the image distribution in a progressive manner while initializing with a simple distribution and compounding the distribution complexity. Although these models have unlocked new applicabilities, the sampling mechanism of diffusion does not offer means to extract image-specific semantic representation, which is inherently provided by auto-encoders. The encoding component of auto-encoders enables mapping between a specific image and its latent space, thereby offering explicit means of enforcing structures in the latent space. By integrating an encoder with the diffusion model, we establish an auto-encoding formulation, which learns image-specific representations and offers means to organize the latent space. In this work, First, we devise a mechanism to structure the latent space of a diffusion auto-encoding model, towards recognizing region-specific cellular patterns in brain images. We enforce the representations to regress positional information of the patches from high-resolution images. This creates a conducive latent space for differentiating tissue types of the brain. Second, we devise an unsupervised tear artifact restoration technique based on neighborhood awareness, utilizing latent representations and the constrained generation capability of diffusion models during inference. Third, through representational guidance and leveraging the inference time steerable noising and denoising capability of diffusion, we devise an unsupervised JPEG artifact restoration technique.

Problem

• Integrate encoder with diffusion model for image-specific semantic representation
• Structure latent space to recognize brain region-specific cellular patterns
• Develop unsupervised artifact restoration techniques for brain images

Innovation

• Integrates encoder with diffusion model for auto-encoding
• Enforces latent space structure with positional regression
• Unsupervised artifact restoration using diffusion generation