no code implementations • 25 May 2020 • Gengyan Zhao, Mary E. Meyerand, Rasmus M. Birn
Compared with the conventional Bayesian neural network with Monte Carlo dropout, results of the proposed method reach a significant lower RMSE with a p-value of 0. 0186.
no code implementations • 18 May 2020 • Gengyan Zhao, Fang Liu, Jonathan A. Oler, Mary E. Meyerand, Ned H. Kalin, Rasmus M. Birn
The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine.
no code implementations • 18 May 2020 • Gengyan Zhao, Gyujoon Hwang, Cole J. Cook, Fang Liu, Mary E. Meyerand, Rasmus M. Birn
Hence, in this study we propose to predict gender from multiple scales of brain FC with deep learning, which can extract full FC patterns as features.
no code implementations • 19 Feb 2019 • Yilin Liu, Gengyan Zhao, Brendon M. Nacewicz, Nagesh Adluru, Gregory R. Kirk, Peter A Ferrazzano, Martin Styner, Andrew L. Alexander
However, most of the previous deep learning work does not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the amygdala and its subregions.