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

• Published in: Patterns (New York, N.Y.) Vol. 2; no. 11; p. 100367
• Main Authors: Edwards, Parker, Skruber, Kristen, Milićević, Nikola, Heidings, James B., Read, Tracy-Ann, Bubenik, Peter, Vitriol, Eric A.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 12.11.2021; Elsevier
• Subjects: actin cytoskeleton; fluorescence microscopy; image classification; image segmentation; machine learning; persistence landscapes; persistent homology; topological data analysis; actin cytoskeleton; image classification; image segmentation; topological data analysis; persistent homology; persistence landscapes; DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems; machine learning; fluorescence microscopy

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 de...