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

arXiv: https://arxiv.org/abs/2508.17666 · HTML: https://mlsanity.com/paper/2508.17666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [DiffusionInst: Diffusion Model for Instance Segmentation](https://mlsanity.com/paper/2212.02773/) (2022)
- [Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation](https://mlsanity.com/paper/2206.13632/) (2022)
- [PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation](https://mlsanity.com/paper/2402.19286/) (2024)
- [ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation](https://mlsanity.com/paper/2312.06458/) (2023)
- [HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis](https://mlsanity.com/paper/2407.00596/) (2024)
- [Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology](https://mlsanity.com/paper/2411.16961/) (2024)
- [Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining](https://mlsanity.com/paper/2206.00123/) (2022)
- [Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology](https://mlsanity.com/paper/2203.05847/) (2022)
- [Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology](https://mlsanity.com/paper/2408.09278/) (2024)
- [Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation](https://mlsanity.com/paper/2101.07654/) (2021)

## Built on by (most cited first, 1 of 1)

- [MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations](https://mlsanity.com/paper/2605.28261/) (2026)

## Abstract (arXiv)

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.