Addressing the Critical Issues of Deep Learning in Medical Imaging

Since being named as one of the top 10 breakthrough technologies of 2013, deep learning has hit the headlines repeatedly, with new applications emerging rapidly. In particular, deep learning techniques have proven to be powerful tools for a range of computer vision tasks, including medical imaging. Accurate diagnosis of disease depends on the acquisition and interpretation of medical images, which is still usually undertaken by humans. Using machines instead is expected to leave less room for human error that is usually due to subjectivity, variations in expertise and opinion of interpreters, and fatigue in physicians. View a selection of presentations from the 2017 Deep Learning in Healthcare Summit in London here, or contact Chloe cpang@re-work.co to sign up for a video membership.