• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Deep feature representations for variable-sized regions of ınterest in breast histopathology

      Thumbnail
      View / Download
      2.8 Mb
      Author(s)
      Mercan, Caner
      Aygüneş, Bulut
      Aksoy, Selim
      Mercan, Ezgi
      Shapiro, L. G.
      Weaver, D. L.
      Elmore, J. G.
      Date
      2021
      Source Title
      IEEE Journal of Biomedical and Health Informatics
      Print ISSN
      2168-2194
      Publisher
      IEEE
      Volume
      25
      Issue
      6
      Pages
      2041 - 2049
      Language
      English
      Type
      Article
      Item Usage Stats
      131
      views
      273
      downloads
      Abstract
      Objective: Modeling variable-sized regions of interest (ROIs) in whole slide images using deep convolutional networks is a challenging task, as these networks typically require fixed-sized inputs that should contain sufficient structural and contextual information for classification. We propose a deep feature extraction framework that builds an ROI-level feature representation via weighted aggregation of the representations of variable numbers of fixed-sized patches sampled from nuclei-dense regions in breast histopathology images. Methods: First, the initial patch-level feature representations are extracted from both fully-connected layer activations and pixel-level convolutional layer activations of a deep network, and the weights are obtained from the class predictions of the same network trained on patch samples. Then, the final patch-level feature representations are computed by concatenation of weighted instances of the extracted feature activations. Finally, the ROI-level representation is obtained by fusion of the patch-level representations by average pooling. Results: Experiments using a well-characterized data set of 240 slides containing 437 ROIs marked by experienced pathologists with variable sizes and shapes result in an accuracy score of 72.65% in classifying ROIs into four diagnostic categories that cover the whole histologic spectrum. Conclusion: The results show that the proposed feature representations are superior to existing approaches and provide accuracies that are higher than the average accuracy of another set of pathologists. Significance: The proposed generic representation that can be extracted from any type of deep convolutional architecture combines the patch appearance information captured by the network activations and the diagnostic relevance predicted by the class-specific scoring of patches for effective modeling of variable-sized ROIs.
      Keywords
      Digital pathology
      Breast histopathology
      Deep feature representation
      Weakly supervised learning
      Region of interest classification
      Permalink
      http://hdl.handle.net/11693/75895
      Published Version (Please cite this version)
      https://dx.doi.org/10.1109/JBHI.2020.3036734
      Collections
      • Department of Computer Engineering 1561
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy