Previous Research: Machine Learning for Medical Applications

We explore unsupervised representation learning to not only reduce labeling bias inherent in supervised image-based medical diagnosis, but also allow data-driven discovery of novel pathological, physiological, and camera-related domains.  We also explore machine learning to generate realistic healthy and tumor medical scans to study human visual perception in radiology.