RAE-Net: A Deep Learning System for Staging of Estrous Cycle

Histopathological examination of animal tissue by pathologists forms a crucial part of preclinical drug development. Machine learning techniques have been utilized to develop increasingly reliable and accurate analytical solutions in tissue imaging in recent years. Accurate assessment of the estrous cycle in rats and mice is important in evaluating pathogenesis and identifying potential test article related toxicity in toxicologic studies. In this paper, we present a Deep-Learning based framework for the classification of different stages of the Estrous cycle using whole slide images (WSI) of Hematoxylin & Eosin (H&E) stained sections from the Wistar rat vagina. We present an encoder-decoder convolution neural network, RAE-Net, based on three key criteria: Residual blocks for the decoder, Attention gate in the skip connection, and EfficientNetB4 for the encoder backbone. We show that the architecture achieves significant performance improvement over state-of-the-art segmentation architecture. The proposed estrous staging system could advance the pathology workflow in female preclinical reproductive toxicology studies.