Machine Learning

Since 2004, the lab has placed significant emphasis on the use of machine learning methods, especially in neuroimaging. This work has led to indices and patterns related to Alzheimer’s disease, Schizophrenia, brain aging, and brain development, amongst others. Several methods have been explored, including linear and nonlinear SVMs, non-negative factorization methods, generative-discriminative learning, deep learning amongst others.


Multi-atlas, multi-warp methods have been main-stream in the lab, allowing for very accurate automated labeling of brain MRI and other types of images


The lab has developed deformable registration methods for normal and abnormal anatomies, the latter incorporating approaches for simultaneously estimating lesions and adapting deformable registration accordingly.