  Latent Diffusion Models (LDMs) can generate high-fidelity images from noise,
offering a promising approach for augmenting histopathology images for training
cancer grading models. While previous works successfully generated
high-fidelity histopathology images using LDMs, the generation of image tiles
to improve prostate cancer grading has not yet been explored. Additionally,
LDMs face challenges in accurately generating admixtures of multiple cancer
grades in a tile when conditioned by a tile mask. In this study, we train
specific LDMs to generate synthetic tiles that contain multiple Gleason Grades
(GGs) by leveraging pixel-wise annotations in input tiles. We introduce a novel
framework named Self-Distillation from Separated Conditions (DISC) that
generates GG patterns guided by GG masks. Finally, we deploy a training
framework for pixel-level and slide-level prostate cancer grading, where
synthetic tiles are effectively utilized to improve the cancer grading
performance of existing models. As a result, this work surpasses previous works
in two domains: 1) our LDMs enhanced with DISC produce more accurate tiles in
terms of GG patterns, and 2) our training scheme, incorporating synthetic data,
significantly improves the generalization of the baseline model for prostate
cancer grading, particularly in challenging cases of rare GG5, demonstrating
the potential of generative models to enhance cancer grading when data is
limited.
