B1 Vertaisarvioimaton kirjoitus tieteellisessä lehdessä
Avoiding a replication crisis in deep-learning-based bioimage analysis
Tekijät: Laine Romain F., Arganda-Carreras Ignacio, Henriques Ricardo, Jacquemet Guillaume
Kustantaja: Springer Nature
Julkaisuvuosi: 2021
Lehti:Nature Methods
Tietokannassa oleva lehden nimiNATURE METHODS
Lehden akronyymi: NAT METHODS
Vuosikerta: 18
Numero: 10
Aloitussivu: 1136
Lopetussivu: 1144
Sivujen määrä: 9
eISSN: 1548-7105
DOI: https://doi.org/10.1038/s41592-021-01284-3
Tiivistelmä
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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