Semi-Supervised Representation Learning for Segmentation on Medical Volumes and Sequences

Benefiting from the massive labeled samples, deep learning-based segmentation methods have achieved great success for two dimensional natural images. However, it is still a challenging task to segment high dimensional medical volumes and sequences, due to the considerable efforts for clinical expert...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 42; H. 12; S. 3972 - 3986
Hauptverfasser:	Chen, Zejian, Zhuo, Wei, Wang, Tianfu, Cheng, Jun, Xue, Wufeng, Ni, Dong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Annotations Biomedical imaging Coders contrastive learning Datasets Decoding Deep learning Feature maps Humans Image Processing, Computer-Assisted Image segmentation Male Medical volume segmentation Pelvis Performance enhancement Prostate Representation learning Representations Segmentation Semantics semi-supervised Semi-supervised learning Supervised Machine Learning Task analysis Teaching methods Training
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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