标题： 基于双网络的半监督医学图像分割的不确定性引导伪标签方法增强 显示英文标题

标题： Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling

摘要： 尽管监督医学图像分割模型表现出色，但在现实情况下依赖大量标记数据是不切实际的。半监督学习方法旨在通过伪标签生成利用未标记数据来缓解这一挑战。然而，现有的半监督分割方法仍然受到噪声伪标签和特征空间内监督不足的困扰。为了解决这些挑战，本文提出了一种基于双网络架构的新型半监督3D医学图像分割框架。具体而言，我们研究了一个交叉一致性增强模块，使用交叉伪标签和熵过滤监督来减少噪声伪标签，同时我们设计了一个动态加权策略，通过不确定性感知机制（即Kullback-Leibler散度）调整伪标签的贡献。此外，我们使用自监督对比学习机制，通过有效区分可信和不确定预测，将不确定体素特征与可靠类别原型对齐，从而减少预测不确定性。在三个3D分割数据集Left Atrial、NIH Pancreas和BraTS-2019上进行了广泛的实验。与最先进的方法相比，所提出的方法在各种设置下表现出优越的性能（例如，在Left Atrial数据集上使用10%标记数据时Dice得分为89.95%）。此外，通过消融实验进一步验证了所提出模块的有效性。 显示英文摘要

摘要： Despite the remarkable performance of supervised medical image segmentation models, relying on a large amount of labeled data is impractical in real-world situations. Semi-supervised learning approaches aim to alleviate this challenge using unlabeled data through pseudo-label generation. Yet, existing semi-supervised segmentation methods still suffer from noisy pseudo-labels and insufficient supervision within the feature space. To solve these challenges, this paper proposes a novel semi-supervised 3D medical image segmentation framework based on a dual-network architecture. Specifically, we investigate a Cross Consistency Enhancement module using both cross pseudo and entropy-filtered supervision to reduce the noisy pseudo-labels, while we design a dynamic weighting strategy to adjust the contributions of pseudo-labels using an uncertainty-aware mechanism (i.e., Kullback-Leibler divergence). In addition, we use a self-supervised contrastive learning mechanism to align uncertain voxel features with reliable class prototypes by effectively differentiating between trustworthy and uncertain predictions, thus reducing prediction uncertainty. Extensive experiments are conducted on three 3D segmentation datasets, Left Atrial, NIH Pancreas and BraTS-2019. The proposed approach consistently exhibits superior performance across various settings (e.g., 89.95\% Dice score on left Atrial with 10\% labeled data) compared to the state-of-the-art methods. Furthermore, the usefulness of the proposed modules is further validated via ablation experiments.