The overall purpose of the ML4MI initiative is to foster interdisciplinary collaboration between machine learning (ML) experts and medical imaging researchers at the University of Wisconsin, in order to develop and apply state-of-the-art ML solutions to challenging problems in medical imaging. This initiative responds to rapidly growing interest in ML techniques within medical imaging research, due to the unprecedented potential to solve challenging problems in areas such as image reconstruction, image processing, and computer-aided diagnosis.
A regular seminar series began in February 2018, and includes 1) seminars describing technical developments in ML with potential biomedical applications, 2) seminars by local or external Radiology researchers, describing problems that may benefit from ML approaches and ongoing projects involving ML techniques, and 3) seminars by biomedical researchers (not in Radiology), describing pioneering experiences applying ML in their fields of study. The seminar location will alternate between ECB/WID and SMPH/WIMR. These seminars will also provide an opportunity for UW researchers to become familiar with researchers “on the other side of campus.”
ONE-DAY WORKSHOP: OCTOBER 5 – Registration Now OPEN
This workshop will be held on October 5, 2018 at the UW Fluno Center. The one-day workshop will further facilitate face-to-face communication and discussion between researchers from various backgrounds and encourage collaboration. The event will include major presentations by leaders in the fields of ML and Radiology, discussion panels, and poster presentations by junior researchers and students.
Register for the ML4MI 2018 workshop
Pilot grant proposals – RFA now available
The purpose of this pilot grant program is to foster interdisciplinary collaboration between ML experts and medical imaging clinicians and researchers at the University of Wisconsin’s Departments of Radiology and Medical Physics, and College of Engineering. Specific topics of interest include the development and characterization of novel ML methods with significant medical imaging applications, and the development and validation of new imaging applications for state-of-the-art ML methods.