mlsanityData to 7 Oct 2026

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

arXiv 2202.00087 ↗First posted 31 Jan 2022eess.IV cs.CV q-bio.QMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 31 Jan 2022.

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14 of 34 references.

References · 14

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  3. 2016Identity Mappings in Deep Residual Networkscited by 1,960
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  8. 2019Micro-Batch Training with Batch-Channel Normalization and Weight Standardizationcited by 45
  9. 2020A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysiscited by 45
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  11. 2018Unsupervised Hard Example Mining from Videos for Improved Object Detectioncited by 30
  12. 2020CircleNet: Anchor-free Detection with Circle Representationcited by 19
2 more
  1. 2020EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathologycited by 4
  2. 2020Instance Segmentation for Whole Slide Imaging: End-to-End or Detect-Then-Segmentcited by 2

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