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Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks.

B. Saghafi, G. Murugesan, E. Davenport, B. Wagner, J. Urban, M. Kelley, D. Jones, A. Powers, C. Whitlow, J. Stitzel, J. Maldjian, and A. Montillo. Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750E. SPIE, (2018)

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Colin Davenport

Genomic and metagenomic application of microbial genome signaturesC. Davenport. MedH Hannover, (2009)

Thomas Winchester Davenport

Elizabeth Pilgramm

Elizabeth Wainwright

Elizabeth Mukidjam

Other publications of authors with the same name

Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain.S. Natarajan, B. Saha, S. Joshi, A. Edwards, T. Khot, E. Davenport, K. Kersting, C. Whitlow, and J. Maldjian. Int. J. Machine Learning & Cybernetics, 5 (5): 659-669 (2014)MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.A. Treacher, P. Garg, E. Davenport, R. Godwin, A. Proskovec, L. Bezerra, G. Murugesan, B. Wagner, C. Whitlow, J. Stitzel and 2 other author(s). NeuroImage, (2021)Single season changes in resting state network power and the connectivity between regions distinguish head impact exposure level in high school and youth football players.G. Murugesan, B. Saghafi, E. Davenport, B. Wagner, J. Urban-Hobson, M. Kelley, D. Jones, A. Powers, C. Whitlow, J. Stitzel and 2 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750F. SPIE, (2018)Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks.B. Saghafi, G. Murugesan, E. Davenport, B. Wagner, J. Urban, M. Kelley, D. Jones, A. Powers, C. Whitlow, J. Stitzel and 2 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750E. SPIE, (2018)Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode.J. Maldjian, E. Davenport, and C. Whitlow. NeuroImage, (2014)Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography.P. Garg, E. Davenport, G. Murugesan, B. Wagner, C. Whitlow, J. Maldjian, and A. Montillo. PRNI, page 1-4. IEEE, (2017)Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography.P. Garg, E. Davenport, G. Murugesan, B. Wagner, C. Whitlow, J. Maldjian, and A. Montillo. MICCAI (3), volume 10435 of Lecture Notes in Computer Science, page 374-381. Springer, (2017)BrainNET: Inference of Brain Network Topology Using Machine Learning.G. Murugesan, C. Yogananda, S. Nalawade, E. Davenport, B. Wagner, W. Kim, and J. Maldjian. Brain Connect., 10 (8): 422-435 (2020)MEG Sensor Selection for Neural Speech Decoding.D. Dash, A. Wisler, P. Ferrari, E. Davenport, J. Maldjian, and J. Wang. IEEE Access, (2020)Automatic identification of successful memory encoding in stereo-eeg of refractory, mesial temporal lobe epilepsy.A. Famili, G. Murugesan, E. Davenport, J. Germi, B. Wagner, B. Lega, and A. Montillo. ISBI, page 587-590. IEEE, (2017)

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Disambiguation of "Davenport, Elizabeth M."