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Challenges and opportunities in bioimage analysis

Nature Methods volume 20, pages 958–961 (2023)Cite this article

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Advanced imaging techniques provide holistic observations of complicated biological phenomena across multiple scales while posing great challenges to data analysis. We summarize recent advances and trends in bioimage analysis, discuss current challenges toward better applicability, and envisage new possibilities.

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Fig. 1: Supervised and self-supervised or unsupervised learning for image analysis.
Fig. 2: Using large language models for analyzing bioimaging data.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62088102, 62222508).

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Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, China

    Xinyang Li, Yuanlong Zhang, Jiamin Wu & Qionghai Dai

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  1. Xinyang Li
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  2. Yuanlong Zhang
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  3. Jiamin Wu
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  4. Qionghai Dai
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Contributions

All authors contributed equally to this work.

Corresponding authors

Correspondence to Jiamin Wu or Qionghai Dai.

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The authors declare no competing interests.

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Li, X., Zhang, Y., Wu, J. et al. Challenges and opportunities in bioimage analysis. Nat Methods 20, 958–961 (2023). https://doi.org/10.1038/s41592-023-01900-4

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