Machine Learning in Brain MR Multicenter Datasets, Mariana Bento, PhD

Speaker(s): 
Mariana Bento, PhD

Abstract:
Magnetic resonance (MR) as well as other imaging modalities have been used in a large number of clinical and research studies for the analysis and quantification of important structures and the detection of abnormalities. In this context, machine learning is playing an increasingly important role in the development of automated tools for aiding in image quantification, patient diagnosis and follow-up. Normally, these techniques require large, heterogeneous datasets to provide accurate and generalizable results. Large, multi-center studies, for example, can provide such data. Images acquired at different centers, however, can present varying characteristics due to differences in acquisition parameters, site procedures and scanners configuration. Machine learning techniques may be robust and reliable, assuring that the findings are answering the proposed research question, and not only due imaging variability. In this talk, I am going to present some of my recent work on this area, using both traditional approaches (hand-crafted texture features) and deep-learning approaches, with special focus on development and evaluation of multicenter studies generated from collaboration between different research institutes.