Mike Marsh
Mike Marsh
Object Research Systems
Verified email at - Homepage
Cited by
Cited by
Digital rock physics benchmarks—Part I: Imaging and segmentation
H Andrä, N Combaret, J Dvorkin, E Glatt, J Han, M Kabel, Y Keehm, ...
Computers & Geosciences 50, 25-32, 2013
Digital rock physics benchmarks—Part II: Computing effective properties
H Andrä, N Combaret, J Dvorkin, E Glatt, J Han, M Kabel, Y Keehm, ...
Computers & Geosciences 50, 33-43, 2013
Structural clusters of evolutionary trace residues are statistically significant and common in proteins
S Madabushi, H Yao, M Marsh, DM Kristensen, A Philippi, ME Sowa, ...
Journal of molecular biology 316 (1), 139-154, 2002
Application of deep learning convolutional neural networks for internal tablet defect detection: high accuracy, throughput, and adaptability
X Ma, N Kittikunakorn, B Sorman, H Xi, A Chen, M Marsh, A Mongeau, ...
Journal of Pharmaceutical Sciences 109 (4), 1547-1557, 2020
Dragonfly as a platform for easy image-based deep learning applications
R Makovetsky, N Piche, M Marsh
Microscopy and microanalysis 24 (S1), 532-533, 2018
X-ray CT and laboratory measurements on glacial till subsoil cores: assessment of inherent and compaction-affected soil structure characteristics
M Lamandé, D Wildenschild, FE Berisso, A Garbout, M Marsh, P Moldrup, ...
Soil Science 178 (7), 359-368, 2013
Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning
A Badran, D Marshall, Z Legault, R Makovetsky, B Provencher, N Piché, ...
Journal of Materials Science 55, 16273-16289, 2020
Steps toward automated deprocessing of integrated circuits
EL Principe, N Asadizanjani, D Forte, M Tehranipoor, R Chivas, ...
ISTFA 2017: Proceedings from the 43rd International Symposium for Testing …, 2017
Correlative X-ray and electron microscopy for multi-scale characterization of heterogeneous shale reservoir pore systems
J Goral, I Miskovic, J Gelb, M Marsh
AAPG Special Volumes, 2016
Poromechanics investigation at pore-scale using digital rock physics laboratory
S Zhang, N Saxena, P Barthelemy, M Marsh, G Mavko, T Mukerji
Proc., The Proceedings of 2011 COMSOL Conference in Stuttgart, 2011
Deep learning convolutional neural networks for pharmaceutical tablet defect detection
X Ma, N Kittikunakorn, B Sorman, H Xi, A Chen, M Marsh, A Mongeau, ...
Microscopy and Microanalysis 26 (S2), 1606-1609, 2020
Simplifying and streamlining large-scale materials image processing with wizard-driven and scalable deep learning
B Provencher, N Piché, M Marsh
Microscopy and Microanalysis 25 (S2), 402-403, 2019
Dragonfly SegmentationTrainer-A General and User-Friendly Machine Learning Image Segmentation Solution
N Piche, I Bouchard, M Marsh
Microscopy and Microanalysis 23 (S1), 132-133, 2017
Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part I: Super-resolution enhancement using a 3D CNN
A Roslin, M Marsh, N Piche, B Provencher, TR Mitchell, IA Onederra, ...
Minerals Engineering 188, 107748, 2022
Forget about cleaning up your micrographs: deep learning segmentation is robust to image artifacts
P Dong, B Provencher, N Basim, N Piché, M Marsh
Microscopy and Microanalysis 26 (S2), 1468-1469, 2020
Automated voice system and method
H Hutchinson, M Marsh, W McMaster
US Patent App. 12/190,643, 2010
Dragonfly as a Flexible Platform for Interpreting and Processing Hyperspectral and other High-dimensional Images
N Piche, F Cote, E Yen, M Marsh
Microscopy and Microanalysis 24 (S1), 560-561, 2018
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
JE Heebner, C Purnell, RK Hylton, M Marsh, MA Grillo, MT Swulius
JoVE (Journal of Visualized Experiments), e64435, 2022
Phantoms Improve Robustness of Deep Learning Automated Segmentation in Cryotomography
J Heebner, C Purnell, M Marsh, M Swulius
Microscopy and Microanalysis 28 (S1), 1226-1228, 2022
Workflow Automation and Portability Enable High Throughput Image Processing and Segmentation for Cell Biology Systems
B Provencher, R Makovetsky, E Yen, N Piché, M Marsh
Microscopy and Microanalysis 25 (S2), 1388-1389, 2019
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