  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.
