On July 23rd and July 25th, the Department of Radiology hosted two Deep Learning Bootcamps as part of the Machine Learning for Medical Imaging initiative. The two bootcamps served nearly 50 interested students, post docs, scientists, and faculty from several UW departments including Radiology, Medical Physics, Electrical and Computer Engineering, Biomedical Engineering, and others to provide hands-on experience in working with deep learning in the context of medical imaging.
The principal organizers and instructors of the event were Tyler Bradshaw, PhD, Jacob Johnson, MS, and Alan McMillan, PhD, from the Department of Radiology and Kevin Johnson, PhD, from Departments of Medical Physics and Radiology.
While the course included discussion of the underpinnings of neural and deep learning networks, the focus was to provide hands-on exercises for participants to gain experience in creating and working with deep learning networks. “The development of deep learning presents a whole new avenue for the analysis of medical images and the way we perform our research, and it is important to introduce these skills to our students” said Dr. McMillan. The hands-on exercises demonstrated the capabilities of deep learning in areas such as detection of disease from chest radiographs, determination of MRI modality, segmentation of lung CT images, conversion of T1-weighted MR images into T2-weighted images, and reconstruction of MR k-space data using a deep learning network. The source code for the exercises has been posted on GitHub. 
The Machine Learning for Medical Imaging initiative is a series of lectures and events which strives to foster interdisciplinary collaboration between machine learning experts and medical imaging researchers at the University of Wisconsin, in order to develop and apply state-of-the-art machine learning solutions to challenging problems in medical imaging.