[Publication]RAE-Net: a deep learning system for staging of estrous cycle
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.
linkAn 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.
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.
