Dual Diversity and Pseudo‐Label Correction Learning for Semi‐Supervised Medical Image Segmentation

ABSTRACT Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data. Current mainstream methods usually adopt two sub‐networks and encourage the two models to make consistent predictions for the same segmentat...

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Veröffentlicht in:	International journal of imaging systems and technology Jg. 35; H. 5
Hauptverfasser:	Du, Guangxing, Wu, Rui, Xu, Jinming, Zeng, Xiang, Xiong, Shengwu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.09.2025 Wiley Subscription Services, Inc
Schlagworte:	Bias co‐training Datasets Feature extraction Image segmentation Labels Learning medical image segmentation Medical imaging Networks pseudo‐labeling Regularization semi‐supervised learning
ISSN:	0899-9457, 1098-1098
Online-Zugang:	Volltext
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