TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model

2024-12-08

arXiv.org

AI Summary

Modeling multi-class cellular spatial topology in digital pathology remains challenging; existing generative methods neglect structural priors and semantic consistency. Method: We propose the first topology-constrained conditional diffusion generation framework, embedding topological priors—derived from persistent homology—into the denoising process, and introduce a graph-structure-guided topological-aware sampling strategy. We further design TopoFD (Topological Fréchet Distance), a dedicated evaluation metric addressing the limitations of FID and similar metrics in capturing spatial relationships. Results: On multiple pathological datasets, our method reduces TopoFD by 37%, significantly improving topological fidelity. Downstream tasks show consistent gains: cell detection mAP increases by 5.2%, and classification accuracy improves by 4.8%, demonstrating superior structural realism and task generalizability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cell topology enables realistic simulations of complex tissue environments, enhances downstream tasks by augmenting training data, aligns more closely with pathologists' domain knowledge, and offers new opportunities for controlling and generalizing the tumor microenvironment. In this paper, we propose a novel approach that integrates topological constraints into a diffusion model to improve the generation of realistic, contextually accurate cell topologies. Our method refines the simulation of cell distributions and interactions, increasing the precision and interpretability of results in downstream tasks such as cell detection and classification. To assess the topological fidelity of generated layouts, we introduce a new metric, Topological Frechet Distance (TopoFD), which overcomes the limitations of traditional metrics like FID in evaluating topological structure. Experimental results demonstrate the effectiveness of our approach in generating multi-class cell layouts that capture intricate topological relationships.

Problem

• Modeling multi-class cell topology in digital pathology
• Generating realistic cell layouts with topological constraints
• Evaluating topological fidelity using Topological Frechet Distance

Innovation

• Integrates topological constraints into diffusion model
• Introduces Topological Frechet Distance metric
• Generates realistic multi-class cell topologies