双监督非对称协同训练用于半监督医学领域泛化
Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization
AI总结：
针对半监督医学图像分割中标注与未标注数据间存在领域偏移的挑战，提出双监督非对称协同训练框架，通过特征级监督与辅助任务提升跨领域泛化性能。
摘要：
半监督领域泛化（SSDG）在医学图像分割中为测试时泛化到未见领域提供了一种有前景的解决方案，解决了领域偏移挑战并最小化标注成本。然而，传统的SSDG方法假设训练集中每个源领域都有标注和未标注数据，这一条件在实践中并不总是满足。训练集中有限标注与领域偏移共存是一个普遍问题。因此，本文探索了一个更实际且更具挑战性的场景，即跨领域半监督领域泛化（CD-SSDG），其中标注和未标注训练数据之间存在领域偏移，此外训练集和测试集之间也存在偏移。现有的SSDG方法在这种领域偏移下由于不准确的伪标签而表现出次优性能。为解决此问题，我们提出了一种针对CD-SSDG的新型双监督非对称协同训练（DAC）框架。基于两个子模型提供交叉伪监督的协同训练范式，我们的DAC框架为每个子模型集成了额外的特征级监督和非对称辅助任务。这种特征级监督旨在解决由标注和未标注数据之间领域偏移引起的不准确伪监督，利用丰富特征空间中的互补监督。此外，每个子模型中集成了两个不同的辅助自监督任务，以增强领域不变的判别性特征学习并防止模型崩溃。在真实世界医学图像分割数据集（即Fundus、Polyp和SCGM）上的大量实验证明了所提出的DAC框架的鲁棒泛化能力。
英文摘要：
Semi-supervised domain generalization (SSDG) in medical image segmentation offers a promising solution for generalizing to unseen domains during testing, addressing domain shift challenges and minimizing annotation costs. However, conventional SSDG methods assume labeled and unlabeled data are available for each source domain in the training set, a condition that is not always met in practice. The coexistence of limited annotation and domain shift in the training set is a prevalent issue. Thus, this paper explores a more practical and challenging scenario, cross-domain semi-supervised domain generalization (CD-SSDG), where domain shifts occur between labeled and unlabeled training data, in addition to shifts between training and testing sets. Existing SSDG methods exhibit sub-optimal performance under such domain shifts because of inaccurate pseudolabels. To address this issue, we propose a novel dual-supervised asymmetric co-training (DAC) framework tailored for CD-SSDG. Building upon the co-training paradigm with two sub-models offering cross pseudo supervision, our DAC framework integrates extra feature-level supervision and asymmetric auxiliary tasks for each sub-model. This feature-level supervision serves to address inaccurate pseudo supervision caused by domain shifts between labeled and unlabeled data, utilizing complementary supervision from the rich feature space. Additionally, two distinct auxiliary self-supervised tasks are integrated into each sub-model to enhance domain-invariant discriminative feature learning and prevent model collapse. Extensive experiments on real-world medical image segmentation datasets, i.e., Fundus, Polyp, and SCGM, demonstrate the robust generalizability of the proposed DAC framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
