摘要
由于注释图像的昂贵和稀缺,基于半监督学习的医学图像分割受到了广泛关注,而如何有效利用无标记数据则成为一个极具挑战的任务。为了充分利用无标记数据,同时解决标记数据和无标记数据之间的经验分布不匹配问题,设计了一种基于多尺度特征融合网络的两阶段分割模型。该模型在第一阶段使用标记数据训练一个教师模型,第二阶段联合无标记数据共同训练学生模型。为了提升教师模型的鲁棒性,使用复制-粘贴增强策略来增加数据的多样性。为了缓解在第二阶段生成的伪标签带来的错误指导问题,引入基于分类噪声过程假设的置信学习,减少伪标签引起的潜在偏差。在两个公开器官数据集上进行了综合实验和消融实验,结果表明所提出的模型实现了高精度分割。
Medical image segmentation based on semi-supervised learning has attracted extensive attention because of the high cost and scarcity of annotated images.Effectively leveraging unlabeled data remains a challenging task.This paper proposes a two-stage segmentation model based on a multi-scale feature fusion network to make use of unlabeled data,and address the empirical distribution mismatch between labeled and unlabeled data.The model uses labeled data to train a teacher model in the first stage and both labeled and unlabeled data are used to co-train a student model in the second stage.To improve the robustness of the teacher model,a copy-paste strategy is employed to increase data diversity.To alleviate the misguidance problem caused by the pseudo-labels generated in the second stage,confidence learning based on an assumption of classified noised process is introduced,thereby reducing the potential bias caused by pseudo-labels.Extensive experiments and ablation studies on two publicly available organ datasets demonstrate that the proposed model achieves high-precision segmentation.
作者
黄田田
马秀丽
黄微
HUANG Tiantian;MA Xiuli;HUANG Wei(School of Communication&Information Engineering,Shanghai University,Shanghai 200444,China)
出处
《应用科学学报》
北大核心
2025年第5期808-816,共9页
Journal of Applied Sciences
基金
国家自然科学基金(No.61771299)。
关键词
半监督学习
器官分割
多尺度特征融合
semi-supervised learning
organ segmentation
multi-scale feature fusion
