Recent developments in artificial intelligence research have resulted in tremendous success in computer vision, natural language processing and medical imaging tasks, often reaching human or superhuman performance. In this thesis, I further developed artificial intelligence methods based on convolutional neural networks with a special focus on the automated analysis of brain magnetic resonance imaging scans (MRI). I showed that efficient artificial intelligence systems can be created using only minimal supervision, by reducing the quantity and quality of annotations used for training. I applied those methods to the automated assessment of the burden of enlarged perivascular spaces, brain structural changes that may be related to dementia, stroke, multiple sclerosis, and sleep.

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This research was funded by The Netherlands Organisation for Health Research and Development (ZonMw) Project104003005.
M. de Bruijne (Marleen) , M.W. Vernooij (Meike) , W.J. Niessen (Wiro)
Erasmus University Rotterdam
hdl.handle.net/1765/126586
Department of Medical Informatics

Dubost, F. (2020, May 8). Artificial Intelligence with Light Supervision: Application to Neuroimaging. Retrieved from http://hdl.handle.net/1765/126586