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Dec 1, 2023 · We introduce a novel method called cross-domain consistency training (CDCT) as an unsupervised domain adaptation technique (UDA) for semantic ...
This study leverages labeled L3 slices i.e., source domain to reliably predict unlabeled lumbar region slices other than L3 i.e., target domain. We use Cross- ...
This study leverages labeled L3 slices i.e., source domain to reliably predict unlabeled lumbar region slices other than L3 i.e., target domain. We use Cross- ...
This study leverages labeled L3 slices i.e., source domain to reliably predict unlabeled lumbar region slices other than L3 i.e., target domain. We use Cross- ...
Article "Volumetric Body Composition Through Cross-Domain Consistency Training for Unsupervised Domain Adaptation" Detailed information of the J-GLOBAL is ...
Volumetric Body Composition Through Cross-Domain Consistency Training for Unsupervised Domain Adaptation · TransUNet-Lite: A Robust Approach to Cell Nuclei ...
May 18, 2023 · We propose SDC-UDA, a simple yet effective volumetric UDA framework for slice-direction continuous cross-modality medical image segmentation.
Missing: Body Composition Consistency
In this work, we report a novel unsupervised domain adaptation framework for cross-modality liver segmentation via joint adversarial learning and self-learning.
Missing: Volumetric Body Composition
In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models.
Missing: Body | Show results with:Body
Dong et al. (2021) proposed an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training (PAST). During training, pixel ...