TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning.

ABSTRACT: Recent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20-30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image

SUBMITTER: Edwards P 

PROVIDER: S-EPMC8600226 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature