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Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
Machine learning approaches for image classification in developmental biology and clinical embryology
Camilla Mapstone1, Berenika Plusa1
1Faculty of Biology, Medicine and Health (FBMH), Division of Developmental Biology & Medicine, Michael Smith Building, Oxford Road, University of Manchester, Manchester M13 9PT, UK.
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The rapid increase in the amount of available biological data together with increasing computational power and innovative new machine learning algorithms has resulted in great potential for machine learning approaches to revolutionise image analysis in developmental biology and clinical embryology. In this Spotlight, we provide an introduction to machine learning for developmental biologists interested in incorporating machine learning techniques into their research. We give an overview of essential machine learning concepts and models and describe a few recent examples of how these techniques can be used in developmental biology. We also briefly discuss latest advancements in the field and how it might develop in the future.

