Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images

Rasoul Sali, Sodiq Adewole, Lubaina Ehsan, Lee A. Denson, Paul Kelly, Beatrice C. Amadi, Lori Holtz, Syed Asad Ali, Sean R. Moore, Sana Syed, Donald E. Brown

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

5 Scopus citations

Abstract

Deep convolutional neural networks (CNNs) have been successful for a wide range of computer vision tasks including image classification. A specific area of application lies in digital pathology for pattern recognition in tissue-based diagnosis of gastrointestinal (GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among different gut diseases. Addressing this problem with a flat model which assumes all classes (parts of the gut and their diseases) are equally difficult to distinguish leads to an inadequate assessment of each class. Since hierarchical model restricts classification error to each sub-class, it leads to a more informative model compared to a flat model. In this paper we propose to apply hierarchical classification of biopsy images from different parts of the GI tract and the receptive diseases within each. We embedded a class hierarchy into the plain VGGNet to take advantage of the hierarchical structure of its layers. The proposed model was evaluated using an independent set of image patches from 373 whole slide images. The results indicate that hierarchical model can achieve better results compared to the flat model for multi-category diagnosis of GI disorders using histopathological images.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728153827
DOIs
StatePublished - Nov 2020
Event8th IEEE International Conference on Healthcare Informatics, ICHI 2020 - Virtual, Oldenburg, Germany
Duration: Nov 30 2020Dec 3 2020

Publication series

Name2020 IEEE International Conference on Healthcare Informatics, ICHI 2020

Conference

Conference8th IEEE International Conference on Healthcare Informatics, ICHI 2020
Country/TerritoryGermany
CityVirtual, Oldenburg
Period11/30/2012/3/20

Keywords

  • Coarse categories
  • Fine classes
  • Gastrointestinal disorders
  • Hierarchical deep convolutional neural network
  • Histopathological images
  • Multi-category diagnosis

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