Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. The two-stage structure complements with the uncertainty regularization strategy to avoid introducing extra modules in solving uncertainties and the aggregation mechanisms enable multi-scale and multi-stage feature integration. Comprehensive experimental results over the MoNuSeg and CRAG datasets show that our PG-FANet outperforms other state-of-the-art methods and our semi-supervised learning framework yields competitive performance with a limited amount of labeled data.

翻译：获取像素级标注在组织学研究等需要领域专业知识的应用中常常受到限制。目前已发展出多种半监督学习方法以应对有限的真实标注，例如流行的教师-学生模型。然而，现有方法尚未充分利用学生模型内部的层级预测不确定性（内部不确定性）及图像预测不确定性（区间不确定性）。为解决这些问题，我们首先提出一种新颖的区间与内部不确定性正则化方法，用于度量并约束教师-学生架构中的区间与内部不一致性。同时，我们提出一种新的两阶段网络——伪掩膜引导特征聚合网络（PG-FANet）作为分割模型。该两阶段结构与不确定性正则化策略形成互补，避免了为解决不确定性问题而引入额外模块，且聚合机制实现了多尺度与多阶段特征融合。在MoNuSeg和CRAG数据集上的全面实验结果表明，我们的PG-FANet优于其他最先进方法，且半监督学习框架在标注数据有限的情况下仍能取得具有竞争力的性能。