Paper ID: 2406.16900

Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation

Irina Zhang, Jim Denholm, Azam Hamidinekoo, Oskar Ålund, Christopher Bagnall, Joana Palés Huix, Michal Sulikowski, Ortensia Vito, Arthur Lewis, Robert Unwin, Magnus Soderberg, Nikolay Burlutskiy, Talha Qaiser

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.

Submitted: May 30, 2024