Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network

Popular semi-supervised medical image segmentation networks often suffer from error supervision from unlabeled data since they usually use consistency learning under different data perturbations to regularize model training. These networks ignore the relationship between labeled and unlabeled data,...

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Veröffentlicht in:	IEEE transactions on medical imaging 2023-05, Vol.42 (5), p.1-1
Hauptverfasser:	Lei, Tao, Zhang, Dong, Du, Xiaogang, Wang, Xuan, Wan, Yong, Nandi, Asoke K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adversarial learning Computer networks Consistency Convolution Dynamic convolution Image processing Image Processing, Computer-Assisted Image segmentation Learning Medical diagnosis Medical image segmentation Medical imaging Networks Perturbation Pixels Semi-supervised learning Spine Supervised Machine Learning Training Uncertainty
Online-Zugang:	Volltext
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