Research

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.

The lab has a great deal of activity in the field of computational oncology, spanning across various anatomies with primary emphasis on brain gliomas and lung cancer. Current focus is to provide precision diagnostics by characterizing tumor heterogeneity via machine learning based imaging signatures, thereby leading to optimized, personalized cancer treatment plans, as well as to develop tools for computational pathology and integrated diagnostics.

Covered here is Aging & Alzheimer’s Disease, Schizophrenia, and Brain Development