Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Lu, Yuzhe, Yang, Haichun, Asad, Zuhayr, Zhu, Zheyu, Yao, Tianyuan, Xu, Jiachen, Fogo, Agnes B., Huo, Yuankai
Institutions: Vanderbilt University, Vanderbilt University Medical Center
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G4094152990
OpenAlex ID: W4225879121