# An Investigation into Glomeruli Detection in Kidney H&E and PAS Images using YOLO

arXiv: https://arxiv.org/abs/2307.13199 · HTML: https://mlsanity.com/paper/2307.13199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https://mlsanity.com/paper/1704.06857/) (2017)
- [A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis](https://mlsanity.com/paper/2001.00258/) (2020)
- [New Descriptor for Glomerulus Detection in Kidney Microscopy Image](https://mlsanity.com/paper/1506.05920/) (2015)
- [Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks](https://mlsanity.com/paper/1702.01816/) (2017)

## Abstract (arXiv)

Context: Analyzing digital pathology images is necessary to draw diagnostic conclusions by investigating tissue patterns and cellular morphology. However, manual evaluation can be time-consuming, expensive, and prone to inter- and intra-observer variability. Objective: To assist pathologists using computerized solutions, automated tissue structure detection and segmentation must be proposed. Furthermore, generating pixel-level object annotations for histopathology images is expensive and time-consuming. As a result, detection models with bounding box labels may be a feasible solution. Design: This paper studies. YOLO-v4 (You-Only-Look-Once), a real-time object detector for microscopic images. YOLO uses a single neural network to predict several bounding boxes and class probabilities for objects of interest. YOLO can enhance detection performance by training on whole slide images. YOLO-v4 has been used in this paper. for glomeruli detection in human kidney images. Multiple experiments have been designed and conducted based on different training data of two public datasets and a private dataset from the University of Michigan for fine-tuning the model. The model was tested on the private dataset from the University of Michigan, serving as an external validation of two different stains, namely hematoxylin and eosin (H&E) and periodic acid-Schiff (PAS). Results: Average specificity and sensitivity for all experiments, and comparison of existing segmentation methods on the same datasets are discussed. Conclusions: Automated glomeruli detection in human kidney images is possible using modern AI models. The design and validation for different stains still depends on variability of public multi-stain datasets.