consistency regularization methods (Kuang Lung Shing)
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Kuang Lung Shing
consistency regularization methods
Consistency Regularization Methods, supplied by Kuang Lung Shing, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/regularization+method/consistency+methods+regularization/pm41544504-36-27-34
Average 86 stars, based on 1 article reviews
Consistency Regularization Methods, supplied by Kuang Lung Shing, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/regularization+method/consistency+methods+regularization/pm41544504-36-27-34
Average 86 stars, based on 1 article reviews
consistency regularization methods - by Bioz Stars,
2026-09
86/100 stars
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other:Article Title: ReTri: Progressive domain bridging via representation disentanglement and triple-level consistency-driven feature alignment for unsupervised domain adaptive medical image segmentation. Article Snippet: Unsupervised domain adaptation (UDA) in medical image segmentation presents significant challenges due to substantial cross-domain disparities and the inherent absence of target domain annotations.. In this study, to address these challenges, we propose an end-to-end progressive domain bridging framework based on representation disentanglement and triple-level consistency-driven feature alignment, referred to as ReTri, that synergistically integrates a representation disentanglement-based image alignment (RDIA) module with a novel triple-level consistency-driven feature alignment (TCFA) module.. In particular, the RDIA module aims to establish an initial domain bridge by decoupling and aligning fundamental visual disparities through disentangled representation learning, while the novel TCFA module hierarchically bridges remaining cross-domain semantic discrepancies and feature distribution disparities via two novel consistency-driven alignment mechanisms: 1) attention-guided semantics-level consistency alignment, where we purposely design a bi-attentive semantic feature extraction (BSFE) component coupled with an attention-adaptive semantic consistency (ASC) loss function, facilitating dynamic alignment of high-level semantic representations across domains, and 2) multi-view dual-level mixing consistency alignment, consisting of Feature-Cut consistent self-ensembling (FCCS) and Trans-Cut consistent self-ensembling (TCCS) components. |