Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.

ABSTRACT:

Background

Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach-SYNTA-for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.

Methods

The SYNTA method employs a fully parametric approach to create photo-realistic synthetic train

SUBMITTER: Mill L 

PROVIDER: S-EPMC11885816 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature