Möckl Leonhard, Roy Anish R, Moerner W E
Department of Chemistry, Stanford University, Stanford, CA 94305, USA.
Biomed Opt Express. 2020 Feb 27;11(3):1633-1661. doi: 10.1364/BOE.386361. eCollection 2020 Mar 1.
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.
在过去十年中，基于深度学习的数据分析方法在所有科学领域都受到了相当大的关注。近年来，这一趋势已经延伸到单分子领域。在本综述中，我们将概述深度学习在单分子成像实验中的应用所做出的重大贡献。此外，我们将描述导致现代深度学习方法发展的历史事件，总结深度学习的基本概念，并强调合适的数据构成对于获得准确、无偏差结果的重要性。
