Avoiding a replication crisis in deep-learning-based bioimage analysis

Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the...

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Vydáno v:Nature methods Ročník 18; číslo 10; s. 1136 - 1144
Hlavní autoři: Laine, Romain F, Arganda-Carreras, Ignacio, Henriques, Ricardo, Jacquemet, Guillaume
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Nature Publishing Group 01.10.2021
Témata:
Algorithms
Biomedical Research - methods
Biomedical Research - standards
Computational Biology - methods
Computational Biology - standards
Deep learning
Deep Learning - standards
Image analysis
Image processing
Image Processing, Computer-Assisted - standards
Learning algorithms
Machine learning
Medical imaging
Microscopy - methods
Microscopy - standards
New technology
ISSN:1548-7091, 1548-7105, 1548-7105
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Shrnutí:Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the potential for inappropriate use is raising concerns among the research community. In this Comment, we discuss key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. We describe how results obtained using deep learning can be validated and propose what should, in our view, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility. We hope this perspective will foster further discussion among developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure the appropriate use of this transformative technology.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-021-01284-3