Disentangled representations of microscopy images

2025-06-25

AI Summary

To address the critical challenge of poor interpretability in deep learning models for microscopic image analysis—where high accuracy often compromises semantic transparency—this paper proposes a synthetic-data-driven disentangled representation transfer learning framework. Our approach is the first to integrate disentangled representation learning (DRL) with synthetic data pretraining, explicitly separating biologically meaningful latent factors. It enables cross-domain transfer across three real-world microscopic image domains: plankton, yeast vacuoles, and human cells. Experiments demonstrate that the method maintains high classification accuracy (average improvement of 0.8%) while substantially enhancing biological interpretability: visualization and expert evaluation confirm that key cellular structures—such as vacuole boundaries and nuclear morphology—are consistently and independently encoded in the learned representations. This work establishes a novel paradigm for deploying interpretable AI in biomedical image analysis.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Enhance interpretability in microscopy image classification
• Trade-off between accuracy and interpretability in DRL
• Apply DRL to diverse microscopic image domains

Innovation

• Disentangled Representation Learning for microscopy
• Transfer learning from synthetic data
• Balances accuracy and interpretability