Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation

Artem Pimkin, Gleb Makarchuk, Vladimir Kondratenko, Maxim Pisov, Egor Krivov, Mikhail Belyaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    20 Citations (Scopus)

    Abstract

    In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images. To validate the developed approaches, we conducted experiments with Breast Cancer Histology Challenge dataset and obtained 90% accuracy for the 4-class tissue classification task.

    Original languageEnglish
    Title of host publicationImage Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings
    EditorsBart ter Haar Romeny, Fakhri Karray, Aurelio Campilho
    PublisherSpringer Verlag
    Pages877-886
    Number of pages10
    ISBN (Print)9783319929996
    DOIs
    Publication statusPublished - 2018
    Event15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal
    Duration: 27 Jun 201829 Jun 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10882 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference15th International Conference on Image Analysis and Recognition, ICIAR 2018
    Country/TerritoryPortugal
    CityPovoa de Varzim
    Period27/06/1829/06/18

    Keywords

    • Convolutional networks
    • Digital pathology
    • Ensembles

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