Collaborative learning of cross-channel clinical attention for radiotherapy-related esophageal fistula prediction from ct

Hui Cui, Yiyue Xu, Wanlong Li, Linlin Wang, Henry Duh

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Early prognosis of the radiotherapy-related esophageal fistula is of great significance in making personalized stratification and optimal treatment plans for esophageal cancer (EC) patients. The effective fusion of diagnostic consideration guided multi-level radiographic visual descriptors is a challenging task. We propose an end-to-end clinical knowledge enhanced multi-level cross-channel feature extraction and aggregation model. Firstly, clinical attention is represented by contextual CT, segmented tumor and anatomical surroundings from nine views of planes. Then for each view, a Cross-Channel-Atten Network is proposed with CNN blocks for multi-level feature extraction, cross-channel convolution module for multi-domain clinical knowledge embedding at the same feature level, and attentional mechanism for the final adaptive fusion of multi-level cross-domain radiographic features. The experimental results and ablation study on 558 EC patients showed that our model outperformed the other methods in comparison with or without multi-view, multi-domain knowledge, and multi-level attentional features. Visual analysis of attention maps shows that the network learns to focus on tumor and organs of interests, including esophagus, trachea, and mediastinal connective tissues.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages212-220
Number of pages9
ISBN (Print)9783030597092
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12261 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20

Keywords

  • Cross channel attention
  • CT
  • Esophageal fistula prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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