Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation
<|reference_start|>Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation: 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.<|reference_end|>
@article{zhang2024utilizing,
    title={Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli
  Segmentation},
    author={Irina Zhang, Jim Denholm, Azam Hamidinekoo, Oskar {AA}lund,
  Christopher Bagnall, Joana Pal'es Huix, Michal Sulikowski, Ortensia Vito,
  Arthur Lewis, Robert Unwin, Magnus Soderberg, Nikolay Burlutskiy, Talha
  Qaiser},
    journal={arXiv preprint arXiv:2406.16900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16900},
    primaryClass={eess.IV cs.CV cs.LG}
}