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Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease.

E. Bron, S. Klein, J. Papma, L. Jiskoot, V. Venkatraghavan, J. Linders, P. Aalten, P. Deyn, G. Biessels, J. Claassen, H. Middelkoop, M. Smits, W. Niessen, J. van Swieten, W. van der Flier, I. Ramakers, and A. van der Lugt. CoRR, (2020)

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Jan Geert Meents

Jan Geert Kunst

Kai Marc Geertsema

Jakob Geerthsen

Philipp Geertsen

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Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change.C. Fiford, C. Sudre, H. Pemberton, P. Walsh, E. Manning, I. Malone, J. Nicholas, W. Bouvy, O. Carmichael, G. Biessels and 2 other author(s). Neuroinformatics, 18 (3): 429-449 (2020)Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH).A. Luca, S. Karayumak, A. Leemans, Y. Rathi, S. Swinnen, J. Gooijers, A. Clauwaert, R. Bahr, S. Sandmo, N. Sochen and 5 other author(s). NeuroImage, (2022)Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease.E. Bron, S. Klein, J. Papma, L. Jiskoot, V. Venkatraghavan, J. Linders, P. Aalten, P. Deyn, G. Biessels, J. Claassen and 7 other author(s). CoRR, (2020)Automated detection of periventricular veins on 7 T brain MRI.H. Kuijf, W. Bouvy, J. Zwanenburg, M. Viergever, G. Biessels, and K. Vincken. Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94132T. SPIE, (2015)Bayesian Model Selection for Pathological Data.C. Sudre, M. Cardoso, W. Bouvy, G. Biessels, J. Barnes, and S. Ourselin. MICCAI (1), volume 8673 of Lecture Notes in Computer Science, page 323-330. Springer, (2014)Registration of Brain CT Images to an MRI Template for the Purpose of Lesion-Symptom Mapping.H. Kuijf, J. Biesbroek, M. Viergever, G. Biessels, and K. Vincken. MBIA, volume 8159 of Lecture Notes in Computer Science, page 119-128. Springer, (2013)Vascular reactivity in small cerebral perforating arteries with 7 T phase contrast MRI - A proof of concept study.L. Geurts, A. Bhogal, J. Siero, P. Luijten, G. Biessels, and J. Zwanenburg. NeuroImage, (2018)An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease.W. Kang, B. Li, J. Papma, L. Jiskoot, P. Deyn, G. Biessels, J. Claassen, H. Middelkoop, W. van der Flier, I. Ramakers and 2 other author(s). ISIC/Care-AI/MedAGI/DeCaF@MICCAI, volume 14393 of Lecture Notes in Computer Science, page 69-78. Springer, (2023)Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation.C. Sudre, M. Cardoso, W. Bouvy, G. Biessels, J. Barnes, and S. Ourselin. IEEE Trans. Medical Imaging, 34 (10): 2079-2102 (2015)Efficient detection of cerebral microbleeds on 7.0 T MR images using the radial symmetry transform.H. Kuijf, J. de Bresser, M. Geerlings, M. Conijn, M. Viergever, G. Biessels, and K. Vincken. NeuroImage, 59 (3): 2266-2273 (2012)

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Disambiguation of "Biessels, Geert Jan"

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Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease.

Please choose a person to relate this publication to

Jan Geert Meents

Jan Geert Kunst

Kai Marc Geertsema

Jakob Geerthsen

Philipp Geertsen

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Biessels, Geert Jan"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease.

Please choose a person to relate this publication to

Jan Geert Meents

Jan Geert Kunst

Kai Marc Geertsema

Jakob Geerthsen

Philipp Geertsen

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
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Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease.