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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
出版社Institute of Electrical and Electronics Engineers Inc.
ページ6-7
ページ数2
ISBN(電子版)9781665418751
DOI
出版ステータスPublished - 2021 3 9
イベント3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 - Nara, Japan
継続期間: 2021 3 92021 3 11

出版物シリーズ

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

Conference

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

ASJC Scopus subject areas

  • 生体医工学
  • 健康情報学
  • 健康(社会科学)
  • 生化学
  • 人工知能
  • コンピュータ サイエンスの応用

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