Deep learning in single-molecule microscopy: Fundamentals, caveats, and recent developments [Invited]
Möckl L, Roy AR, Moerner WE (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 11
Pages Range: 1633-1661
Journal Issue: 3
DOI: 10.1364/BOE.386361
Abstract
Deep learning-based data analysis methods have gained considerable attention in all fields of science over the last decade. In recent years, this trend has reached the single-molecule community. In this review, we will survey significant contributions of the application of deep learning in single-molecule imaging experiments. Additionally, we will describe the historical events that led to the development of modern deep learning methods, summarize the fundamental concepts of deep learning, and highlight the importance of proper data composition for accurate, unbiased results.
Involved external institutions
United States (USA) (US)How to cite
APA:
Möckl, L., Roy, A.R., & Moerner, W.E. (2020). Deep learning in single-molecule microscopy: Fundamentals, caveats, and recent developments [Invited]. Biomedical Optics Express, 11(3), 1633-1661. https://doi.org/10.1364/BOE.386361
MLA:
Möckl, Leonhard, Anish R. Roy, and W. E. Moerner. "Deep learning in single-molecule microscopy: Fundamentals, caveats, and recent developments [Invited]." Biomedical Optics Express 11.3 (2020): 1633-1661.
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