  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.
