B1 Non-refereed article in a scientific journal
Avoiding a replication crisis in deep-learning-based bioimage analysis
Authors: Laine Romain F., Arganda-Carreras Ignacio, Henriques Ricardo, Jacquemet Guillaume
Publisher: Springer Nature
Publication year: 2021
Journal: Nature Methods
Journal name in source: NATURE METHODS
Journal acronym: NAT METHODS
Volume: 18
Issue: 10
First page : 1136
Last page: 1144
Number of pages: 9
eISSN: 1548-7105
DOI: https://doi.org/10.1038/s41592-021-01284-3(external)
Abstract
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.
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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