DescriptionInterest in quantitative image analysis and visualization in healthcare, from population-level academic research studies to patient-specific analysis has grown precipitously in the recent past. While applications for these tools to large-scale research have found significant support from the research community, opportunities at the clinical level where most healthcare effort and research is executed have been extremely limited, while their potential value has exploded. Interest and awareness of these tools has never been higher, as physicians and researchers have recognized how HPC and visualization can complement and even drive their own work. Mostly recently, the ever-increasing interest in machine learning, and particularly deep learning, to volumetric image analysis has once again begun to challenge both the HPC and medical communities. Lines of communication between these intersecting fields, however, have been extremely limited, stifling engagement and deployment of the most advanced imaging analysis and technology for healthcare research and clinical practice. Our workshop will bring together these disparate communities to engage on problems and solutions encountered in the practice of image analysis and visualization in healthcare. Thought leaders from across the aisle divide will come together to discuss problems where HPC, imaging analysis, and visualization can dramatically affect patient care. In addition, we will invite high-quality publications in key topic areas in medical image analysis to participate as invited speakers with full-length presentations. Lastly, we will hold practical sessions in which selected problems will be solved interactively with the audience, with code samples and iPython notebooks available for distribution to the audience.