Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction

By C. Belthangady, Loic A. Royer. Nature Methods, 2018

487 citations recorded by the source corpus.

Deep learning is becoming an increasingly important tool for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration and super-resolution imaging, and discuss how the latest deep learning research could be applied to other image reconstruction tasks. Despite its successes, deep learning also poses substantial challenges and has limits. We discuss key questions, including how to obtain training data, whether discovery of unknown structures is possible, and the danger of inferring unsubstantiated image details. This Perspective highlights recent applications of deep learning in fluorescence microscopy image reconstruction and discusses future directions and limitations of these approaches.

Subfields: Biophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7dae942104dc8283504ce7a492c9ca12fa119189

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1. Qinfeng Gao
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Citations in the corpus

• Deep learning for smart fish farming: applications, opportunities and challenges (2020, background): “In addition, neural networks can be tricked into producing completely different outputs after imperceptible perturbations are applied to their inputs (Belthangady & Royer, 2019 ; Moosavi-Dezfooli et al., 2016).”
• Deep learning for cellular image analysis (2019, background): “Augmented microscopy is not limited to the association of fluorescence traces with transmitted light images 145 .”