3 papers
eess.IV2026
Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines
Xiangjian Hou, Chao Qin, Chang Ni +3
Global token mixing, implemented via self-attention or state-space sequence models, has become a popular model design choice for MRI restoration. However, MRI restoration tasks dif…
cs.CV2025
Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation
Xin Wang, Yin Guo, Jiamin Xia +5
Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by sou…
cs.CV2025
RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation
Xin Wang, Yin Guo, Kaiyu Zhang +4
This work presents a novel Bayesian framework for unsupervised domain adaptation (UDA) in medical image segmentation. While prior works have explored this clinically significant ta…