NAS-SGAN: A Semi-Supervised Generative Adversarial Network Model for Atypia Scoring of Breast Cancer Histopathological Images.

Nuclear atypia scoring (NAS), forms a significant factor in determining individualized treatment plans and also for the prognosis of the disease. Automation of cancer grading using quantitative image-based analysis of histopathological images can circumvent the shortcomings of the prevailing manual grading and can assist the pathologists in cancer diagnosis. However, developing such a robust classifier model require sufficient amount of annotated data, while the labeled histopathological images are scarce and expensive to procure as annotation forms a time-consuming and laborious task.