Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks

Nima Tajbakhsh, Suryakanth R. Gurudu, Jianming Liang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    55 Citations (Scopus)

    Abstract

    Computer-aided polyp detection in colonoscopy videos has been the subject of research for over the past decade. However, despite significant advances, automatic polyp detection is still an unsolved problem. In this paper, we propose a new polyp detection method based on a unique 3-way image presentation and convolutional neural networks. Our method learns a variety of polyp features such as color, texture, shape, and temporal information in multiple scales, enabling a more accurate polyp localization. Given a polyp candidate, a set of convolution neural networks - each specialized in one type of features - are applied in the vicinity of the candidate and then their results are aggregated to either accept or reject the candidate. Our experimental results based on our collection of videos, which to our knowledge is the largest annotated polyp database, shows a remarkable performance improvement over the state-of-the-art, significantly reducing the number of false positives in nearly all operating points. In addition, we propose a new performance curve, demonstrating that our new method significantly decreases polyp detection latency, which is defined as the time from the first appearance of a polyp in the video to the time of its first detection by our method.

    Original languageEnglish (US)
    Title of host publicationProceedings - International Symposium on Biomedical Imaging
    PublisherIEEE Computer Society
    Pages79-83
    Number of pages5
    Volume2015-July
    ISBN (Print)9781479923748
    DOIs
    StatePublished - Jul 21 2015
    Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
    Duration: Apr 16 2015Apr 19 2015

    Other

    Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
    CountryUnited States
    CityBrooklyn
    Period4/16/154/19/15

    Fingerprint

    Colonoscopy
    Polyps
    Neural networks
    Convolution
    Textures
    Color
    Databases
    Research

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

    Cite this

    Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2015). Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In Proceedings - International Symposium on Biomedical Imaging (Vol. 2015-July, pp. 79-83). [7163821] IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163821

    Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. / Tajbakhsh, Nima; Gurudu, Suryakanth R.; Liang, Jianming.

    Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 79-83 7163821.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Tajbakhsh, N, Gurudu, SR & Liang, J 2015, Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. in Proceedings - International Symposium on Biomedical Imaging. vol. 2015-July, 7163821, IEEE Computer Society, pp. 79-83, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 4/16/15. https://doi.org/10.1109/ISBI.2015.7163821
    Tajbakhsh N, Gurudu SR, Liang J. Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July. IEEE Computer Society. 2015. p. 79-83. 7163821 https://doi.org/10.1109/ISBI.2015.7163821
    Tajbakhsh, Nima ; Gurudu, Suryakanth R. ; Liang, Jianming. / Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. pp. 79-83
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