17 citations · 35 across the 5 of their papers we have counts for
5 papers
Meta-modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia
Haleh Falakshahi, Victor M. Vergara, Jingyu Liu +13
Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on…
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…
Deep learning for neuroimaging: a validation study
Sergey M. Plis, Devon R. Hjelm, Ruslan Salakhutdinov +1
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience d…
Towards structured sharing of raw and derived neuroimaging data across existing resources
D. B. Keator, K. Helmer, J. Steffener +7
Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is cu…