Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation

Xu Chen, Tianshu Kuang, Hannah Deng, Steve H. Fung, Jaime Gateno, James J. Xia, Pew Thian Yap

Research output: Contribution to journalArticlepeer-review

Abstract

Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-attention domain-adaptative segmentation network (DADASeg-Net) for cross-modality medical image segmentation. The key contribution of DADASeg-Net is a novel dual adversarial attention mechanism, which regularizes the domain adaptation module with two attention maps respectively from the space and class perspectives. Specifically, the spatial attention map guides the domain adaptation module to focus on regions that are challenging to align in adaptation. The class attention map encourages the domain adaptation module to capture class-specific instead of class-agnostic knowledge for distribution alignment. DADASeg-Net shows superior performance in two challenging medical image segmentation tasks.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Medical Imaging
DOIs
StateAccepted/In press - 2022

Keywords

  • Adaptation models
  • Adversarial Learning
  • Annotations
  • Attention Mechanism
  • Feature extraction
  • Image segmentation
  • Medical Image Segmentation
  • Medical diagnostic imaging
  • Semantics
  • Task analysis
  • Unsupervised Domain Adaptation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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