Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-grained quantitative analysis of multiple GGS subtypes (e.g., obsolescent, solidified, and disappearing glomerulosclerosis) is typically a resource extensive manual process. Very few automatic methods, if any, have been developed to bridge this gap for such analytics. In this paper, we present a holistic pipeline to quantify GGS (with both detection and classification) from a whole slide image in a fully automatic manner. In addition, we conduct the fine-grained classification for the sub-types of GGS. Our study releases the open-source quantitative analytical tool for fine-grained GGS characterization while tackling the technical challenges in unbalanced classification and integrating detection and classification.
@article{arxiv.2202.00087,
title = {Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification},
author = {Yuzhe Lu and Haichun Yang and Zuhayr Asad and Zheyu Zhu and Tianyuan Yao and Jiachen Xu and Agnes B. Fogo and Yuankai Huo},
journal= {arXiv preprint arXiv:2202.00087},
year = {2022}
}