Comparative Analysis of Diffusion Generative Models in Computational Pathology

TL;DR

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.

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

Figures (9)

• Figure 1: The process of selecting and magnifying different regions from a histopathology slide to obtain patches with varying Fields of View (FOV). The images demonstrate the detail captured at FOV 600, FOV 400, and FOV 200, representing the data's range of granularity and the model's ability to maintain clarity at different magnification levels.
• Figure 2: Diffusion Generative Models framework used in the pathology dataset: The Training phase (top-right) details the data points' progression from $x_0$ to $x_T$ with noise levels $\epsilon_0$ to $\epsilon_T$, where the model adds noise to the original data and estimates the reverse process guided by class information to understand tissue details. The Sampling phase (bottom-right) reverses the diffusion from a noisy state $x_T$ to the original data point $x_0$, by iteratively denoising using noise predictions $\epsilon'$ and learned parameters, and prompting the class label to generate images of specific tissue types.
• Figure 3: Samples from Cancer Tissue and Normal WSI
• Figure 4: Real vs Generated Images from the FOV 224 (DDPM and LDM): The top row shows a representative image from the real dataset for each tissue type, while the bottom row displays a generated image of the same tissue.
• Figure 5: Real vs Generated Images from the FOV 336 (DDPM and LDM): The top row shows a representative image from the real dataset for each tissue type, while the bottom row displays a generated image of the same tissue.