Conference Proceedings

Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI

Saba Momeni, Amir Fazllolahi, Pierrick Bourgeat, Parnesh Raniga, Paul Yates, Nawaf Yassi, Patricia Desmond, Jurgen Fripp, Yongsheng Gao, Olivier Salvado, A Gooya (ed.), O Goksel (ed.), I Oguz (ed.), N Burgos (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER INTERNATIONAL PUBLISHING AG | Published : 2018


Machine learning applied to medical imaging for lesions detection, such as cerebral microbleeds (CMB) from Magnetic Resonance Imaging (MRI), is challenged by the relatively small datasets available for which only subjective and tedious visual reading is available, and by the low prevalence of lesions (a few in ~10% of a typical elderly cohort) resulting in unbalanced classes. Moreover, the lack of actual ground truth might limit the performance of any machine learning method to that of human performance. Yet, the automatic identification of those lesions is relevant to quantify cerebrovascular burden associated with dementia, such as identifying co-morbidity for Alzheimer’s disease. In this ..

View full abstract