An interpretable DL-based method for diagnosis of H.Pylori infection using gastric X-ray images

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

Abstract

In this paper, we propose an interpretable deep learning-based method for diagnosis of helicobacter pylori (H. pylori) infection using double-contrast upper gastric barium X-ray images. Based on a transfer learning strategy, a deep convolutional neural network (DCNN) model, named Inception-ResNet-v2, was trained and tested to classify gastric X-ray images into two classes: infected and non-infected. In addition, an visualization technique was utilized to generate a saliency map that can indicates which anatomic regions are important for predicting the H. pylori infection. The experimental results demonstrated that the proposed method can achieve a high sensitivity and specificity in diagnosis of H. pylori infection. As a computer-aided diagnosis (CAD) system, the proposed method is also capable of providing an interpretable diagnosis to explain the relation between the image features and the prediction result.

Original languageEnglish
Title of host publicationLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6-7
Number of pages2
ISBN (Electronic)9781665418751
DOIs
Publication statusPublished - 2021 Mar 9
Event3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 - Nara, Japan
Duration: 2021 Mar 92021 Mar 11

Publication series

NameLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies

Conference

Conference3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021
CountryJapan
CityNara
Period21/3/921/3/11

Keywords

  • CAD
  • DCNN
  • Gastric X-ray
  • H.pylori infection
  • Interpretable AI
  • Transfer learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health(social science)
  • Biochemistry
  • Artificial Intelligence
  • Computer Science Applications

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