  Accurate segmentation of glomerulus instances attains high clinical
significance in the automated analysis of renal biopsies to aid in diagnosing
and monitoring kidney disease. Analyzing real-world histopathology images often
encompasses inter-observer variability and requires a labor-intensive process
of data annotation. Therefore, conventional supervised learning approaches
generally achieve sub-optimal performance when applied to external datasets.
Considering these challenges, we present a semi-supervised learning approach
for glomeruli segmentation based on the weak-to-strong consistency framework
validated on multiple real-world datasets. Our experimental results on 3
independent datasets indicate superior performance of our approach as compared
with existing supervised baseline models such as U-Net and SegFormer.
