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

Autor:	Zhang, Irina, Denholm, Jim, Hamidinekoo, Azam, Ålund, Oskar, Bagnall, Christopher, Huix, Joana Palés, Sulikowski, Michal, Vito, Ortensia, Lewis, Arthur, Unwin, Robert, Soderberg, Magnus, Burlutskiy, Nikolay, Qaiser, Talha
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted to MIDL'24
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.16900 Zobrazit plný text záznamu View this record from Arxiv