剪裁(形态学)
计算机科学
图像分割
人工智能
图像(数学)
分割
算法
计算机视觉
模式识别(心理学)
语言学
哲学
作者
Chendong Qin,Yongxiong Wang,Jiapeng Zhang
标识
DOI:10.1016/j.cmpb.2024.108278
摘要
Training convolutional neural networks based on large amount of labeled data has made great progress in the field of image segmentation. However, in medical image segmentation tasks, annotating the data is expensive and time-consuming because pixel-level annotation requires experts in the relevant field. Currently, the combination of consistent regularization and pseudo labeling-based semi-supervised methods has shown good performance in image segmentation. However, in the training process, a portion of low-confidence pseudo labels are generated by the model. And the semi-supervised segmentation method still has the problem of distribution bias between labeled and unlabeled data. The objective of this study is to address the challenges of semi-supervised learning and improve the segmentation accuracy of semi-supervised models on medical images.
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