Detecting Invasive Ductal Carcinoma with Semi-Supervised Conditional GANs
This addresses the need for automated detection of IDC to assist pathologists in cancer diagnosis, but it appears incremental as it builds on existing GAN and CNN methods.
The paper tackles the problem of automatically detecting invasive ductal carcinoma (IDC), a common breast cancer type, by proposing a semi-supervised conditional GAN algorithm, which improves scores over a baseline CNN.
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.