Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification
This work addresses a domain-specific problem for medical researchers and clinicians in nephrology by providing an open-source tool to automate GGS analysis, though it is incremental as it builds on existing automation efforts in medical imaging.
The authors tackled the problem of automating fine-grained quantitative analysis of multiple global glomerulosclerosis (GGS) subtypes from whole slide images, which is typically manual and resource-intensive, by developing a holistic pipeline that achieves fully automatic detection and classification.
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.