SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation
Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffering from semantically unaware feature alignment that results in poor distributional fidelity and from pseudo-label validation that disregards global anatomical constraints, thus failing to prevent the formation of globally implausible structures. To address these issues, we propose SHAPE (Structure-aware Hierarchical U
Record details
Published: 23 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Environment
Retrieved: 14 July 2026
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ethics.ai (23 March 2026), “SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation,” evidence record 6850, https://ethics.ai/record/6850 (originally published by arXiv).
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