Deep Learning in Image Cytometry: A Review

Anindya Gupta, Philip J. Harrison, Håkan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Ida Maria Sintorn, C. Wählby

    Research output: Contribution to journalReview Articlepeer-review

    58 Citations (Scopus)
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    Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data.

    Original languageEnglish
    Pages (from-to)366-380
    JournalCytometry Part A
    Issue number4
    Publication statusPublished - 2019
    Publication typeA2 Review article in a scientific journal


    • biomedical image analysis
    • cell analysis
    • convolutional neural networks
    • deep learning
    • image cytometry
    • machine learning
    • microscopy

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