ArticleIEEE transactions on medical imaging2022
Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Novel Image Segmentation Framework Using Adaptive Region-Based Unsupervised Machine Learning Mechanism.Journal of clinical ultrasound : JCU · 2026Article
- A Lightweight Modified Adaptive UNet for Nucleus Segmentation.Sensors (Basel, Switzerland) · 2026Article
- Multi-scale anatomical regularization for domain-adaptive segmentation of pelvic CBCT images.Medical physics · 2024Article
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
- Deep neural network-based robust framework for automated skin lesion segmentation and analysis.Digital healthArticle
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7 authors.
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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.
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