Validating retinaNet for the object detection-based mitosis detection in the MIDOG challenge

Abstract

Mitosis detection is critical in histopathology for accurate diagnosis and prognosis of tumors, a topic of particular interest underscored by recent challenges. In this study, we focus on developing deep learning (DL) solutions to confront this challenge within the framework of the MIDOG challenge. Leveraging the newest MIDOG challenge dataset, the MIDOG++ dataset, we explore the efficacy of object detection models. Specifically, the RetinaNet model using fastai and PyTorch frameworks. We replicate and validate the reference work, RetinaNet, using fastai, and we propose the RetinaNet model using PyTorch. Through rigorous training and evaluation, we analyze the performance of these models in detecting mitotic figures, crucial for automating histopathological analysis and improving diagnostic accuracy. Our study demonstrates the effectiveness of the RetinaNet model in mitosis detection within histopathological images. Obtaining favorable F1 scores across the different scenarios and analyzing the relationship between different tumor types.