DSST: A dual student model guided student–teacher framework for semi-supervised medical image segmentation

对偶(语法数字) 计算机科学 分割 图像(数学) 图像分割 计算机视觉 人工智能 双重目的 数学教育 心理学 机械工程 艺术 文学类 工程类
作者
Boliang Li,Yan Wang,Yaming Xu,Chen Wu
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:90: 105890-105890 被引量:7
标识
DOI:10.1016/j.bspc.2023.105890
摘要

Numerous semi-supervised learning methods are emerging in medical image segmentation to reduce the dependency of deep learning models on pixel-level annotation data. Consistency regularization methods based on the Student–Teacher structure have achieved brilliant results in this domain. However, the current structures are unable to resolve the tight weight coupling satisfactorily between the teacher and student model, which leads to a decrease in the segmentation performance. In this paper, we propose a novel and practical semi-supervised learning framework, Dual-Student-Single-Teacher (DSST), to alleviate this problem. Particularly, the DSST framework consists of three segmentation models with the identical structure but different initial parameters, one serves as the teacher model and others as the student models, which employs an alternating manner to update the teacher model parameters. For the DSST framework, we present different supervised modes to sufficiently explore the enhancement of consistency regularization for model segmentation performance. Furthermore, we also introduce abundant and efficient input and feature perturbations for the proposed method. Finally, we evaluate our framework in three public medical image segmentation tasks, including Pancreas-CT, LA dataset, and cardiac segmentation on the ACDC dataset. Extensive experiments demonstrate that, compared with eight other superior semi-supervised methods, the DSST method obtains state-of-the-art segmentation performance and is an effective and generalizable framework. Code is available at https://github.com/LBL0704/DSST.
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