Rectified Mixed-Label Learning for Semi-Supervised Medical Image Segmentation

Semi-supervised medical image segmentation (SSMIS) has gained increasing attention due to its potential to alleviate the manual annotation burden. However, existing works face two key challenges: i) how to deal with the information loss caused by learning labeled and unlabeled data in an inconsisten...

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Veröffentlicht in:	Proceedings (IEEE International Conference on Multimedia and Expo) S. 1 - 6
Hauptverfasser:	An, Zeyu, Chen, Zichong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 30.06.2025
Schlagworte:	Biomedical imaging Contrastive learning Data models Faces Image segmentation Manuals Medical Image Segmentation Mixed-Label Supervision Noise Prototype Learning Prototypes Reliability Semantics
ISSN:	1945-788X
Online-Zugang:	Volltext
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