Deep learning has already revolutionized the field of computer vision, making practical technologies out of what seemed like science fiction just a few years ago. If these new computer vision systems can reach human-level accuracy in identifying dog breeds or cars, we asked ourselves, might those same systems be capable of learning to identify the disease in medical images? In this talk, I'll discuss the usage of deep learning for medical imaging and share several of our projects in the space of ophthalmology and pathology.
Po-Hsuan (Cameron) Chen is a member of the Google Brain team working on deep learning and healthcare. His primary research interests lie at the intersection of machine learning, deep learning, medical imaging, and computational neuroscience. His research focuses on developing machine learning models for large-scale medical imaging analysis problems, such as cancer detection in histopathology images and multi-subject brain state classification with functional MRI data. Besides, he has also worked on several theoretical and applied machine learning projects in various domains at Princeton, Amazon, Palantir, Intel Labs and Vatic Labs. He received his PhD degree in Electrical Engineering and Neuroscience from Princeton University and BS in Electrical Engineering from National Taiwan University. Po-Hsuan was also a recipient of the Google PhD Fellowship.