URCA: Uncertainty-based region clipping algorithm for semi-supervised medical image segmentation.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 11, 2024

Abstract

BACKGROUND AND OBJECTIVE: 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.

Authors

  • Chendong Qin
    University of Shanghai for Science and Technology, School of Opto-Electronic Information and Computer Engineering, Department of Control Science and Engineering, 516 War Industrial Road, Shanghai 200093, China.
  • Yongxiong Wang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China; Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
  • Jiapeng Zhang
    College of Materials and Chemistry & Chemical Engineering, Chengdu University of Technology, Chengdu, 610059, P. R. China.

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