As humans we feel nothing more viscerally -- in the most literal sense -- than our health. That makes this year's gathering of the Medical Image Computing and Computer Assisted Interventions Society -- MICCAI 2017 -- in Quebec City, Canada, one of the best ways to understand how deep learning is improving the lives of people all around us. The conference brings together leading biomedical scientists, engineers and clinicians to talk about new technologies in medical imaging and computer-assisted intervention, providing an early look at trends poised to sweep through the $6.5 trillion healthcare industry. This will be the group's biggest conference yet, with 1,300 attendees. And deep learning -- which pairs vast quantities of data with sophisticated neural networks to give computers amazing new capabilities -- deserves a lot of the credit, organizers say.

Deep learning is the field of machine learning that studies and develops artificial neural networks capable of learning several layers of representation (features) from raw data. These methods have delivered new levels of performance in the field of computer vision. More recently, they have become popular in medical imaging systems, such as for the segmentation of various types of tissues in medical imagery. In this tutorial, we will provide an introduction to deep learning, covering both theory and practice. On the theory side, we will describe the most common concepts found in today's deep learning research, with a focus on convolutional neural networks.