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arXiv · 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
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Abstract

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.

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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. 2024-05-30. Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation. https://arxiv.org/abs/2406.16900

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