Invasive ductal carcinoma (IDC) comprises nearly 80% of all breast cancers.
The detection of IDC is a necessary preprocessing step in determining the
aggressiveness of the cancer, determining treatment protocols, and predicting
patient outcomes, and is usually performed manually by an expert pathologist.
Here, we describe a novel algorithm for automatically detecting IDC using
semi-supervised conditional generative adversarial networks (cGANs). The
framework is simple and effective at improving scores on a range of metrics
over a baseline CNN.
