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.Clinical relevance- Carcinoma of the prostate is the second most common cancer diagnosed in men, with approximately one in nine men diagnosed in their lifetime. The tumor staging via Gleason score is the most powerful prognostic predictor for prostate cancer patients.