7 citations · 11 across the 5 of their papers we have counts for
5 papers
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
Roberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng +84
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if…
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
Vladimir Belov, Tracy Erwin-Grabner, Ali Saffet Gonul +63
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predict…
Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
Dmitry Petrov, Boris A. Gutman Egor Kuznetsov, Theo G. M. van Erp +70
We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional ne…
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Dmitry Petrov, Boris A. Gutman, Shih-Hua +72
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts ha…
Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model
Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad +13
Large-scale collaborative analysis of brain imaging data, in psychiatry and neu-rology, offers a new source of statistical power to discover features that boost ac-curacy in diseas…