no code implementations • 28 Mar 2024 • Pulkit Khandelwal, Michael Tran Duong, Constanza Fuentes, Amanda Denning, Winifred Trotman, Ranjit Ittyerah, Alejandra Bahena, Theresa Schuck, Marianna Gabrielyan, Karthik Prabhakaran, Daniel Ohm, Gabor Mizsei, John Robinson, Monica Munoz, John Detre, Edward Lee, David Irwin, Corey McMillan, M. Dylan Tisdall, Sandhitsu Das, David Wolk, Paul A. Yushkevich
Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem).
2 code implementations • 21 Mar 2023 • Pulkit Khandelwal, Michael Tran Duong, Shokufeh Sadaghiani, Sydney Lim, Amanda Denning, Eunice Chung, Sadhana Ravikumar, Sanaz Arezoumandan, Claire Peterson, Madigan Bedard, Noah Capp, Ranjit Ittyerah, Elyse Migdal, Grace Choi, Emily Kopp, Bridget Loja, Eusha Hasan, Jiacheng Li, Alejandra Bahena, Karthik Prabhakaran, Gabor Mizsei, Marianna Gabrielyan, Theresa Schuck, Winifred Trotman, John Robinson, Daniel Ohm, Edward B. Lee, John Q. Trojanowski, Corey McMillan, Murray Grossman, David J. Irwin, John Detre, M. Dylan Tisdall, Sandhitsu R. Das, Laura E. M. Wisse, David A. Wolk, Paul A. Yushkevich
Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements.
2 code implementations • 14 Oct 2021 • Pulkit Khandelwal, Shokufeh Sadaghiani, Michael Tran Duong, Sadhana Ravikumar, Sydney Lim, Sanaz Arezoumandan, Claire Peterson, Eunice Chung, Madigan Bedard, Noah Capp, Ranjit Ittyerah, Elyse Migdal, Grace Choi, Emily Kopp, Bridget Loja, Eusha Hasan, Jiacheng Li, Karthik Prabhakaran, Gabor Mizsei, Marianna Gabrielyan, Theresa Schuck, John Robinson, Daniel Ohm, Edward Lee, John Q. Trojanowski, Corey McMillan, Murray Grossman, David Irwin, M. Dylan Tisdall, Sandhitsu R. Das, Laura E. M. Wisse, David A. Wolk, Paul A. Yushkevich
Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy.
2 code implementations • 25 May 2018 • Fady Medhat, David Chesmore, John Robinson
We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds.
1 code implementation • 8 Apr 2018 • Fady Medhat, David Chesmore, John Robinson
Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties.
1 code implementation • 6 Mar 2018 • Fady Medhat, David Chesmore, John Robinson
We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition.
1 code implementation • 18 Feb 2018 • Fady Medhat, David Chesmore, John Robinson
MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.
1 code implementation • 15 Feb 2018 • Fady Medhat, David Chesmore, John Robinson
Deep neural network architectures designed for application domains other than sound, especially image recognition, may not optimally harness the time-frequency representation when adapted to the sound recognition problem.
1 code implementation • 7 Feb 2018 • Fady Medhat, David Chesmore, John Robinson
Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms.
1 code implementation • 16 Jan 2018 • Fady Medhat, David Chesmore, John Robinson
Neural network based architectures used for sound recognition are usually adapted from other application domains such as image recognition, which may not harness the time-frequency representation of a signal.