MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a w...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2024; s. 5851 - 5862
Hlavní autori:	Zhang, Xuzhe, Wu, Yuhao, Angelini, Elsa, Li, Ang, Guo, Jia, Rasmussen, Jerod M., OConnor, Thomas G., Wadhwa, Pathik D., Jackowski, Andrea Parolin, Li, Hai, Posner, Jonathan, Laine, Andrew F., Wang, Yun
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2024
Predmet:	Computational modeling Computer vision generalization Image segmentation Magnetic resonance imaging masked image modeling medical image Pediatrics Reviews self-supervised learning Three-dimensional displays unsupervised domain adaptation
ISSN:	1063-6919, 1063-6919
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