Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification

TL;DR

This paper introduces an automatic, holistic pipeline for detecting and classifying global glomerulosclerosis subtypes in whole slide images, addressing technical challenges in unbalanced data and providing an open-source analytical tool.

Contribution

It presents a novel fully automatic method for fine-grained GGS analysis, including detection and classification of subtypes, with an open-source implementation.

Findings

Successfully detects GGS in whole slide images

Performs fine-grained classification of GGS subtypes

Addresses unbalanced classification challenges

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.