Comparative Analysis of Diffusion Generative Models in Computational Pathology

TL;DR

This paper provides a comprehensive comparison of diffusion generative models in computational pathology, demonstrating their effectiveness in generating high-quality synthetic histopathology images and exploring their impact on model accuracy.

Contribution

It offers the first detailed analysis of diffusion models in pathology, including an ablation study and insights on image size adjustments for varying fields of view.

Findings

DGMs produce high-quality synthetic pathology data.

Adjusting image size can simulate different fields of view.

DGMs enhance model accuracy when combined with real data.

Abstract

Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning applications. Despite their high computational demand, these models are extensively utilized for their superior sample quality and robust mode coverage. While research in diffusion generative models is advancing, exploration within the domain of computational pathology and its large-scale datasets has been comparatively gradual. Bridging the gap between the high-quality generation capabilities of Diffusion Generative Models and the intricate nature of pathology data, this paper presents an in-depth comparative analysis of diffusion methods applied to a pathology dataset. Our analysis extends to datasets with varying Fields of View (FOV), revealing that DGMs are highly effective in producing high-quality synthetic data. An ablative study is also conducted, followed by a detailed discussion on the impact of various methods on the synthesized histopathology images. One striking observation from our experiments is how the adjustment of image size during data generation can simulate varying fields of view. These findings underscore the potential of DGMs to enhance the quality and diversity of synthetic pathology data, especially when used with real data, ultimately increasing accuracy of deep learning models in histopathology. Code is available from https://github.com/AtlasAnalyticsLab/Diffusion4Path