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. ML4MI is generously supported by the UW Departments of Radiology and Medical Physics, and the Grainger Institute for Engineering.
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.”
Pilot grant proposals – 2019 pilot grants awarded
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. In 2018, pilot grants were awarded to the following collaborative teams: – Kevin Johnson, Alejandro Roldan, and Shiva Rudraraju, “Patient specific hemodynamics using machine learning based fusion of MRI measurements and computational fluid dynamics” – Varun Jog and Alan McMillan, “DeepRad: An accessible, open-source tool for deep learning in medical imaging” In 2019, pilot grants were awarded to the following collaborative teams: – Sean Fain and Victor Zavala, “Blending Expert and Machine Learning Using Quantitative Chest CT and Clinical Biomarkers to Predict Asthma Severity and Outcomes” – Meghan Lubner, Varun Jog, and Dane Morgan, “Application of Machine Learning to CT characterization of Renal Cell Carcinoma“
ML4MI Summer Bootcamps
ML4MI hosts bootcamps with the goal of giving participants a rapid, hands-on introduction into the principles and application of machine learning for medical imaging. This bootcamp is supported by the Grainger Institute of Engineering and the Departments of Radiology and Medical Physics. These bootcamps cover the basics of machine learning and applications for image segmentation, classification, and reconstruction. Please sign up for updates for information on future bootcamps! The source material is also available for self study.
Bootcamp Source Materials:
ML4MI Bootcamp Github
Bootcamp Organizer Contacts:
PREVIOUS WORKSHOP: OCTOBER 5, 2018
This workshop was held on October 5, 2018 at the UW Fluno Center. The one-day workshop featured keynote talks by leaders in the fields of ML and Radiology, a seminar on bioethics, a panel of radiologists, and poster presentations by junior researchers.