M^3-GloDets: Multi-Region and Multi-Scale Analysis of Fine-Grained Diseased Glomerular Detection

TL;DR

This paper introduces M^3-GloDet, a comprehensive framework for evaluating detection models across multiple regions, scales, and classes of diseased glomeruli, addressing current gaps in digital renal pathology analysis.

Contribution

The study systematically assesses detection models on diverse regions, scales, and disease classes, providing insights into optimal imaging magnifications and patch sizes for improved accuracy.

Findings

Intermediate patch sizes balance context and efficiency.

Moderate magnifications improve model generalization.

Systematic evaluation reveals strengths and limitations of various models.

Abstract

Accurate detection of diseased glomeruli is fundamental to progress in renal pathology and underpins the delivery of reliable clinical diagnoses. Although recent advances in computer vision have produced increasingly sophisticated detection algorithms, the majority of research efforts have focused on normal glomeruli or instances of global sclerosis, leaving the wider spectrum of diseased glomerular subtypes comparatively understudied. This disparity is not without consequence; the nuanced and highly variable morphological characteristics that define these disease variants frequently elude even the most advanced computational models. Moreover, ongoing debate surrounds the choice of optimal imaging magnifications and region-of-view dimensions for fine-grained glomerular analysis, adding further complexity to the pursuit of accurate classification and robust segmentation. To bridge these gaps, we present M^3-GloDet, a systematic framework designed to enable thorough evaluation of detection models across a broad continuum of regions, scales, and classes. Within this framework, we evaluate both long-standing benchmark architectures and recently introduced state-of-the-art models that have achieved notable performance, using an experimental design that reflects the diversity of region-of-interest sizes and imaging resolutions encountered in routine digital renal pathology. As the results, we found that intermediate patch sizes offered the best balance between context and efficiency. Additionally, moderate magnifications enhanced generalization by reducing overfitting. Through systematic comparison of these approaches on a multi-class diseased glomerular dataset, our aim is to advance the understanding of model strengths and limitations, and to offer actionable insights for the refinement of automated detection strategies and clinical workflows in the digital pathology domain.