分割
稳健性(进化)
计算机科学
人工智能
判别式
模式识别(心理学)
一致性(知识库)
局部一致性
约束满足
生物化学
化学
概率逻辑
基因
作者
Jiajun Lin,Feng Li,Li Li,Lingjie Lin,Dongqi Li,Glen M. Borchert,Jingshan Huang
标识
DOI:10.1109/bibm58861.2023.10385993
摘要
To solve the label scarcity of meibomian gland segmentation in infrared meibography images, a novel framework for semi-supervised meibomian gland segmentation is firstly presented in this paper. Extra mutual feature consistency constraint is added along with the cross pseudo supervision , guiding the model more robustness and discriminative. Meanwhile, cross uncertainty rectification is introduced to avoid noisy labels, further improving the pseudo supervision. Experimental results on an internal dataset reveals that our method yields significant performances using only 10% of the labeled data compared to the fully supervised segmentation, and outperforms the state-of-the art semi-supervised segmentation methods. Combination of mutual consistency regularization and cross uncertainty rectifi-cation guides model to distinguish glands from background well with limited labeled data.
科研通智能强力驱动
Strongly Powered by AbleSci AI