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Convolutional Neural Networks in Advanced Biomedical Imaging Applications

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Deep Learning for Biomedical Data Analysis

Abstract

Deep learning (DL), in particular Convolutional Neural Networks (CNNs), can be used to build powerful quantitative image analysis tools. To take advantage of these capabilities and establish tractable goals and problem solving approaches, it is crucial to understand how both imaging and computational tools have been developed and used together. Different advanced optical imaging methods, whether invasive or non-invasive, are applicable to a wide variety of biomedical research, and CNN algorithms can be tailored to assist with extracting meaningful results from imaging data.

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Authors and Affiliations

  1. Harvard University Biophysics Program, Boston, MA, USA

    Daniel A. Greenfield

  2. Sierra Research S.L.U., Alicante, Spain

    Germán González

  3. Wellman Center for Photomedicine, Boston, MA, USA

    Conor L. Evans

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  1. Daniel A. Greenfield
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  2. Germán González
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  3. Conor L. Evans
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Correspondence to Conor L. Evans .

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  1. Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia

    Mourad Elloumi

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Greenfield, D.A., González, G., Evans, C.L. (2021). Convolutional Neural Networks in Advanced Biomedical Imaging Applications. In: Elloumi, M. (eds) Deep Learning for Biomedical Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71676-9_9

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