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    Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks

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    Workshop paper (790.5Kb)
    Publication Date
    2017-10
    Authors
    Mehanian, Courosh
    Jaiswal, Mayoore
    Delahunt, Charles
    Thompson, Clay
    Horning, Matt
    Hu, Liming
    McGuire, Shawn
    Ostbye, Travis
    Mehanian, Martha
    Wilson, Ben
    Champlin, Cary
    Long, Earl
    Proux, Stephane
    Gamboa, Dionicia
    Chiodini, Peter
    Carter, Jane
    Dhorda, Mehul
    Isaboke, David
    Ogutu, Bernhards
    Oyibo, Wellington
    Villasis, Elizabeth
    Tun, Kyaw Myo
    Bachman, Christine
    Bell, David
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    Other
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    Citation

    Mehanian, C., Jaiswal, M.S., Delahunt, C.B., Thompson, C.M., Horning, M.P., Hu, L., McGuire, S.K., Ostbye, T., Mehanian, M., Wilson, B.K., Champlin, C.R., Long, E., Proux, S., Gamboa, D., Chiodini, P., Carter, J., Dhorda, M., Isaboke, D., Ogutu, B., Oyibo, W., Villasis, E., Tun, K.M., Bachman, C.M., & Bell, D. (2017). Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 116-125.

    Abstract/Overview

    The optical microscope remains a widely-used tool for diagnosis and quantitation of malaria. An automated system that can match the performance of well-trained technicians is motivated by a shortage of trained microscopists. We have developed a computer vision system that leverages deep learning to identify malaria parasites in micrographs of standard, field-prepared thick blood films. The prototype application diagnoses P. falciparum with sufficient accuracy to achieve competency level 1 in the World Health Organization external competency assessment, and quantitates with sufficient accuracy for use in drug resistance studies. A suite of new computer vision techniques—global white balance, adaptive nonlinear grayscale, and a novel augmentation scheme—underpin the system’s state-of-the-art performance. We outline a rich, global training set; describe the algorithm in detail; argue for patient-level performance metrics for the evaluation of automated diagnosis methods; and provide results for P. falciparum.

    Subject/Keywords
    Computer vision system; Micrographs; World Health Organization; Drug resistance; P. falciparum; Optical microscope
    Further Details

    This ICCV Workshop paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the version available on IEEE Xplore.

    Publisher
    CVF
    Permalink
    https://repository.amref.ac.ke/handle/123456789/565
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    • General - GEN [355]

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