Carcino-Net: A Deep Learning Framework for Automated Gleason Grading of Prostate Biopsies

Gleason scoring for prostate cancer grading is a subjective examination and suffers from suboptimal interobserver and intraobserver variability. To overcome these limitations, we have developed an automated system to grade prostate biopsies. We present a novel deep learning architecture Carcino-Net, which improves semantic segmentation performance. The proposed network is a modified FCN8s with ResNet50 backbone. Using Carcino-Net, we not only report best performance in separating the different grades, we also offer greater accuracy over other state-of-the-art frameworks. The proposed system could expedite the pathology workflow in diagnostic laboratories by triaging high-grade biopsies.