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Deep Learning in Image Cytometry: A Review.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.ORCID iD: 0000-0003-3557-4947
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth group)ORCID iD: 0000-0003-4046-9017
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
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2018 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930Article in journal (Refereed) Epub ahead of print
Abstract [en]

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. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Place, publisher, year, edition, pages
2018.
Keywords [en]
biomedical image analysis, cell analysis, convolutional neural networks, deep learning, image cytometry, machine learning, microscopy
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-371631DOI: 10.1002/cyto.a.23701PubMedID: 30565841OAI: oai:DiVA.org:uu-371631DiVA, id: diva2:1273808
Funder
Swedish Foundation for Strategic Research Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2018-12-21

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Gupta, AnindyaWieslander, HåkanPartel, GabrieleSolorzano, LeslieSuveer, AmitSpjuth, OlaSintorn, Ida-MariaWählby, Carolina

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Gupta, AnindyaHarrison, Philip JWieslander, HåkanPartel, GabrieleSolorzano, LeslieSuveer, AmitSpjuth, OlaSintorn, Ida-MariaWählby, Carolina
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Division of Visual Information and InteractionDepartment of Pharmaceutical BiosciencesComputerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLab
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Cytometry Part A
Medical Image Processing

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