Medical Imaging Informatics
Imaging informatics or computer vision as a subfield of AI has been around for several years dealing with how computers can understand from digital images and video sequences. Advanced computer vision algorithms have already demonstrated successful applications in a variety of domains, including medical image interpretation, remote surgery, surveillance systems, security and biometrics, autonomous vehicles, and scene reconstruction, purposing to name a few. There is a list of fascinating problems in applied computer vision in medical imaging, with object detection and localization being one of the most interesting ones. Object detection and localization is now also widely associated with self-driving cars where automatic systems combine computer vision, LIDAR, and GPUs to generate a multidimensional representation of the road with all its participants. It is also commonly used in medical image analysis, video surveillance and monitoring, counting people for general statistics, and computationally analyze customer experience with walking patterns within shopping centers. In this summer school, you will learn -from scratch- how to use advanced computer vision algorithms to tackle the problem of object detection and localization in medical images. We will discuss object detection mechanism(s) in practice with several hands-on-practices starting from manual image annotation to programming and implementation in Python. We, together, will explore what object detection computational vision algorithm is, what is does, and how. The current AI summer school at the University of Pittsburgh is structured such that in addition to attending lectures, the students will be also working in teams on a project assignment.
