Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model

TL;DR

This paper introduces a probabilistic diffusion model to synthesize histology image patches conditioned on mitosis labels, helping to interpret and understand features associated with mitotic figures amidst label uncertainty.

Contribution

It presents a novel use of diffusion models to generate synthetic cell nuclei images conditioned on mitosis labels, aiding interpretability of mitosis features in histology images.

Findings

Generated images reveal key mitosis features like nuclear irregularity and cytoplasm granularity.

Model helps visualize the transition into mitosis, aiding pathologist interpretation.

Provides a new tool for understanding label uncertainty in mitosis detection.

Abstract

Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no ``gold-standard'' independent ground-truth. However, it is well-established that these interpretation based labels are often unreliable, in part, due to differences in expertise levels and human subjectivity. In this paper, our goal is to shed light on the inherent uncertainty of mitosis labels and characterize the mitotic figure classification task in a human interpretable manner. We train a probabilistic diffusion model to synthesize patches of cell nuclei for a given mitosis label condition. Using this model, we can then generate a sequence of synthetic images that correspond to the same nucleus transitioning into the mitotic state. This allows us to identify different image features associated with mitosis, such as cytoplasm granularity, nuclear density, nuclear irregularity and high contrast between the nucleus and the cell body. Our approach offers a new tool for pathologists to interpret and communicate the features driving the decision to recognize a mitotic figure.