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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| Published in: | Nature methods Vol. 18; no. 10; pp. 1136 - 1144 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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United States
Nature Publishing Group
01.10.2021
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| ISSN: | 1548-7091, 1548-7105, 1548-7105 |
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| 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. |
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| AbstractList | 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. |
| Author | Henriques, Ricardo Arganda-Carreras, Ignacio Jacquemet, Guillaume Laine, Romain F |
| Author_xml | – sequence: 1 givenname: Romain F surname: Laine fullname: Laine, Romain F organization: Micrographia Bio, Translation and Innovation Hub, London, UK – sequence: 2 givenname: Ignacio surname: Arganda-Carreras fullname: Arganda-Carreras, Ignacio organization: Donostia International Physics Center (DIPC), San Sebastian, Spain – sequence: 3 givenname: Ricardo surname: Henriques fullname: Henriques, Ricardo organization: Instituto Gulbenkian de Ciência, Oeiras, Portugal – sequence: 4 givenname: Guillaume orcidid: 0000-0002-9286-920X surname: Jacquemet fullname: Jacquemet, Guillaume email: guillaume.jacquemet@abo.fi, guillaume.jacquemet@abo.fi, guillaume.jacquemet@abo.fi organization: Turku Bioimaging, University of Turku and Åbo Akademi University, Turku, Finland. guillaume.jacquemet@abo.fi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34608322$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | Avoiding a replication crisis in deep-learning-based bioimage analysis |
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