The fast development of structural, functional, and metabolic imaging of the human body impacts diagnosis and treatment in medicine. The increasing raw amounts of data and their increasing complexity open the doors for novel approaches for analysis and knowledge extraction. Such novel approaches often incorporate non-imaging data such as genomics, laboratory tests, or measurements from wearable devices. Many of the more complex data require significant processing before they become useful for decision making. Whether planning an operation or comparing populations in neuroscience, medical computing provides a critical foundation for medical research and patient care. The tools and methods used in medical computing are based on concepts from the fields of computer science and mathematics. The core technologies that are used include signal processing, image segmentation and registration, visualization, and biomedical modeling. Statistical approaches and machine learning have always played a role, but more recently, we have seen an increasing impact through the introduction of deep convolutional networks for many tasks. In the end, the goal of medical computing is the extraction of information and, ultimately, medical knowledge from complex raw data.