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Glen Pridham
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Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging
Y Zhang, D Hong, D McClement, O Oladosu, G Pridham, G Slaney
Journal of Neuroscience Methods 353, 109098, 2021
842021
Characterizing structural changes with evolving remyelination following experimental demyelination using high angular resolution diffusion MRI and texture analysis
T Luo, O Oladosu, KS Rawji, P Zhai, G Pridham, S Hossain, Y Zhang
Journal of Magnetic Resonance Imaging 49 (6), 1750-1759, 2019
232019
Strategies for handling missing data that improve Frailty Index estimation and predictive power: lessons from the NHANES dataset
G Pridham, K Rockwood, A Rutenberg
GeroScience 44 (2), 897-923, 2022
112022
Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis
Z Hosseinpour, L Jonkman, O Oladosu, G Pridham, GB Pike, M Inglese, ...
Journal of Neuroscience Methods 379, 109671, 2022
72022
Acoustic radiation force impulse and conventional ultrasound in the prediction of cirrhosis complicating fatty liver: does body mass index independently alter the results?
A Medellin, G Pridham, SJ Urbanski, S Jayakumar, SR Wilson
Ultrasound in Medicine & Biology 45 (12), 3160-3171, 2019
72019
A discrete polar Stockwell transform for enhanced characterization of tissue structure using MRI
G Pridham, MD Steenwijk, JJG Geurts, Y Zhang
Magnetic resonance in medicine 80 (6), 2731-2743, 2018
62018
Efficient representations of binarized health deficit data: the frailty index and beyond
G Pridham, K Rockwood, A Rutenberg
Geroscience 45 (3), 1687-1711, 2023
52023
Evaluation of discrete orthogonal versus polar Stockwell Transform for local multi-resolution texture analysis using brain MRI of multiple sclerosis patients
G Pridham, O Oladosu, Y Zhang
Magnetic Resonance Imaging 72, 150-158, 2020
42020
A metric learning method for estimating myelin content based on T2-weighted MRI from a de-and re-myelination model of multiple sclerosis
G Pridham, S Hossain, KS Rawji, Y Zhang
Plos one 16 (4), e0249460, 2021
12021
Quantitative phase and texture angularity analysis of brain white matter lesions in multiple sclerosis
S Baxandall, S Sharma, P Zhai, G Pridham, Y Zhang
Medical Imaging 2018: Image Processing 10574, 350-356, 2018
12018
Photodisintegration of the Deuteron at 18 MeV using Linearly Polarized Photons
G Pridham
University of Saskatchewan, 2014
12014
Dynamical network stability analysis of multiple biological ages provides a framework for understanding the aging process
G Pridham, AD Rutenberg
The Journals of Gerontology, Series A: Biological Sciences and Medical …, 2024
2024
Network dynamical stability analysis reveals key “mallostatic” natural variables that erode homeostasis and drive age-related decline of health
G Pridham, AD Rutenberg
Scientific Reports 13 (1), 22140, 2023
2023
Network dynamical stability analysis of homeostasis reveals "mallostasis": biological equilibria drifting towards worsening health with age
G Pridham, A Rutenberg
arXiv:2306.12448, 2023
2023
Modelling disease impact: lifespan reduction is greatest for young adults in an exogenous damage model of disease
R Tobin, G Pridham, D Rutenberg, Andrew
arXiv:2305.06808, 2023
2023
The Frailty Index, Missing Data, and Imputation
G Pridham, A Rutenberg
Innovation in Aging 4 (Suppl 1), 923, 2020
2020
Characterizing MS lesion pathology using directional metrics of auto-correlation in MRI
Y Zhang, G Pridham
MULTIPLE SCLEROSIS JOURNAL 24, 432-433, 2018
2018
D (γ, n) H: Photodisintegration of the Deuteron at 18 MeV using Linearly Polarized Photons
G Pridham
2014
Photodisintegration of the Deuteron at 20 MeV using Circularly Polarized Photons
G Pridham
2014
Measurement of neutron recoil polarization in low energy photodisintegration of deuterium
BE Norum, S Tkachenko, RA Lindgren, PN Seo, R Duve, H Arenhövel, ...
2013
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