Disentangled representations of microscopy images
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Microscopy image analysis is fundamental for different applications, from
diagnosis to synthetic engineering and environmental monitoring. Modern
acquisition systems have granted the possibility to acquire an escalating
amount of images, requiring a consequent development of a large collection of
deep learning-based automatic image analysis methods. Although deep neural
networks have demonstrated great performance in this field, interpretability,
an essential requirement for microscopy image analysis, remains an open
challenge.
This work proposes a Disentangled Representation Learning (DRL) methodology
to enhance model interpretability for microscopy image classification.
Exploiting benchmark datasets from three different microscopic image domains
(plankton, yeast vacuoles, and human cells), we show how a DRL framework, based
on transferring a representation learnt from synthetic data, can provide a good
trade-off between accuracy and interpretability in this domain.
