Machine Learning for the Detection of Brain Abnormalities

Activity: Talk or presentationInvited talkAcademic


Magnetic Resonance Imaging as a clinical diagnostic method is one of the greatest innovations of the twentieth century. In Europe, millions of images are made yearly and the number is steadily increasing. Assessment of medical images relies on visual inspection, which can be time-consuming and subjective. Automated machine learning solutions have proven essential for reliable detection and quantification of brain pathology.

Brain abnormalities--associated with stroke, dementia, and aging--have been a key application for machine learning solutions in medical image analysis. Various automated analysis techniques have been developed, to provide quantitative measurements and replace time-consuming, observer-dependent delineation procedures. Such techniques exist or are currently being developed, for white matter hyperintensities (WMH), microbleeds, microinfarcts, and more. A scientific contest demonstrated which approaches work well for these applications and the results will be presented.

Hugo Kuijf is an assistant professor at the Image Sciences Institute, UMC Utrecht.
Period8 Nov 2019
Event titleData Science Day 2019
Event typeConference
Degree of RecognitionNational