DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations

TL;DR

DiffEx is a novel method that uses diffusion models to generate interpretable visual explanations for classifiers, helping identify microscopic cellular differences and advancing biological understanding.

Contribution

This paper introduces DiffEx, a new approach leveraging diffusion models to interpret classifiers and detect subtle cellular variations in biological images.

Findings

Effective in explaining classifiers trained on biological images

Uncovers phenotypic differences in microscopy datasets

Aids in identifying potential biomarkers

Abstract

In recent years, deep learning models have been extensively applied to biological data across various modalities. Discriminative deep learning models have excelled at classifying images into categories (e.g., healthy versus diseased, treated versus untreated). However, these models are often perceived as black boxes due to their complexity and lack of interpretability, limiting their application in real-world biological contexts. In biological research, explainability is essential: understanding classifier decisions and identifying subtle differences between conditions are critical for elucidating the effects of treatments, disease progression, and biological processes. To address this challenge, we propose DiffEx, a method for generating visually interpretable attributes to explain classifiers and identify microscopic cellular variations between different conditions. We demonstrate the effectiveness of DiffEx in explaining classifiers trained on natural and biological images. Furthermore, we use DiffEx to uncover phenotypic differences within microscopy datasets. By offering insights into cellular variations through classifier explanations, DiffEx has the potential to advance the understanding of diseases and aid drug discovery by identifying novel biomarkers.