Deep learning for detecting diseases in gastrointestinal biopsy images

Aman Srivastava, Saurav Sengupta, Sung Jun Kang, Karan Kant, Marium Khan, S. Asad Ali, Sean R. Moore, Beatrice C. Amadi, Paul Kelly, Sana Syed, Donald E. Brown

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

8 Citations (Scopus)

Abstract

Machine learning and computer vision have found applications in medical science and, recently, pathology. In particular, deep learning methods for medical diagnostic imaging can reduce delays in diagnosis and give improved accuracy rates over other analysis techniques. This paper focuses on methods with applicability to automated diagnosis of images obtained from gastrointestinal biopsies. These deep learning techniques for biopsy images may help detect distinguishing features in tissues affected by enteropathies. Learning from different areas of an image, or looking for similar patterns in new images, allow for the development of potential classification or clustering models Techniques like these provide a cutting-edge solution to detecting anomalies. In this paper we explore state of the art deep learning architectures used for the visual recognition of natural images and assess their applicability in medical image analysis of digitized human gastrointestinal biopsy slides.

Original languageEnglish
Title of host publication2019 Systems and Information Engineering Design Symposium, SIEDS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109985
DOIs
Publication statusPublished - Apr 2019
Event2019 Systems and Information Engineering Design Symposium, SIEDS 2019 - Charlottesville, United States
Duration: 26 Apr 2019 → …

Publication series

Name2019 Systems and Information Engineering Design Symposium, SIEDS 2019

Conference

Conference2019 Systems and Information Engineering Design Symposium, SIEDS 2019
Country/TerritoryUnited States
CityCharlottesville
Period26/04/19 → …

Keywords

  • deep learning
  • disease detection
  • machine learning
  • medical imaging

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