The bridging of domains such as deep learning-driven image analysis and biology brings exciting promises of previously impossible discoveries as well as perils of misinterpretation and misapplication. We encourage continual communication between method developers and application scientists that emphases likely pitfalls and provides validation tools in conjunction with new techniques.
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References
Huang, B., Bates, M. & Zhuang, X. Annu. Rev. Biochem. 78, 993–1016 (2009).
Nieuwenhuizen, R. P. J. et al. Nat. Methods 10, 557–562 (2013).
Legant, W. R. et al. Nat. Methods 13, 359–365 (2016).
Lambert, T. J. & Waters, J. C. J. Cell Biol. 216, 53–63 (2017).
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J.W. is funded by the Chan Zuckerberg Initiative.
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Lambert, T., Waters, J. Towards effective adoption of novel image analysis methods. Nat Methods 20, 971–972 (2023). https://doi.org/10.1038/s41592-023-01910-2
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DOI: https://doi.org/10.1038/s41592-023-01910-2
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