📝 SummarXiv

Abstract

Accurate detection of mitotic figures in whole slide histopathological images remains a challenging task due to their scarcity, morphological heterogeneity, and the variability introduced by tissue preparation and staining protocols. The MIDOG competition series provides standardized benchmarks for evaluating detection approaches across diverse domains, thus motivating the development of generalizable deep learning models. In this work, we investigate the performance of two modern one-stage detectors, YOLOv5 and YOLOv8, trained on MIDOG++, CMC, and CCMCT datasets. To enhance robustness, training incorporated stain-invariant color perturbations and texture preserving augmentations. In internal validation, YOLOv5 achieved superior precision, while YOLOv8 provided improved recall, reflecting architectural trade-offs between anchor-based and anchor-free detection. To capitalize on these complementary strengths, we employed an ensemble of the two models, which improved sensitivity without a major reduction in precision. These findings highlight the effectiveness of ensemble strategies built upon contemporary object detectors to advance automated mitosis detection in digital pathology.