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Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. / ENIGMA consortium.
Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Ahmed Abdulkadir; Deepti R. Bathula; Nicha C. Dvornek; Mohamad Habes; Seyed Mostafa Kia; Vinod Kumar; Thomas Wolfers. Springer Science and Business Media Deutschland GmbH, 2022. p. 115-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13596 LNCS).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
ENIGMA consortium 2022,
Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. in A Abdulkadir, DR Bathula, NC Dvornek, M Habes, SM Kia, V Kumar & T Wolfers (eds),
Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13596 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 115-124, 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, Singapore,
18/09/22.
https://doi.org/10.1007/978-3-031-17899-3_12APA
ENIGMA consortium (2022).
Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In A. Abdulkadir, D. R. Bathula, N. C. Dvornek, M. Habes, S. M. Kia, V. Kumar, & T. Wolfers (Eds.),
Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 115-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13596 LNCS). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-031-17899-3_12Vancouver
ENIGMA consortium.
Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In Abdulkadir A, Bathula DR, Dvornek NC, Habes M, Kia SM, Kumar V, Wolfers T, editors, Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 115-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-17899-3_12
Author
ENIGMA consortium. /
Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. editor / Ahmed Abdulkadir ; Deepti R. Bathula ; Nicha C. Dvornek ; Mohamad Habes ; Seyed Mostafa Kia ; Vinod Kumar ; Thomas Wolfers. Springer Science and Business Media Deutschland GmbH, 2022. pp. 115-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{96252ce11a9c43a9b972c3cb4bbce5da,
title = "Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging",
abstract = "We extend the sparse, spatially piecewise-contiguous linear classification framework for mesh-based data to ordinal logistic regression. The algorithm is intended for use with subcortical shape and cortical thickness data where progressive clinical staging is available, as is generally the case in neurodegenerative diseases. We apply the tool to Parkinson{\textquoteright}s and Alzheimer{\textquoteright}s disease staging. The resulting biomarkers predict Hoehn-Yahr and cognitive impairment stages at competitive accuracy; the models remain parsimonious and outperform one-against-all models in terms of the Akaike and Bayesian information criteria.",
keywords = "Imaging biomarker, Neurodegenerative Disease, Ordinal regression",
author = "{ENIGMA consortium} and Yuji Zhao and Laansma, {Max A.} and {van Heese}, {Eva M.} and Conor Owens-Walton and Parkes, {Laura M.} and Ines Debove and Christian Rummel and Roland Wiest and Fernando Cendes and Rachel Guimaraes and Yasuda, {Clarissa Lin} and Wang, {Jiun Jie} and Anderson, {Tim J.} and Dalrymple-Alford, {John C.} and Melzer, {Tracy R.} and Pitcher, {Toni L.} and Reinhold Schmidt and Petra Schwingenschuh and G{\"a}etan Garraux and Mario Rango and Letizia Squarcina and Sarah Al-Bachari and Emsley, {Hedley C.A.} and Klein, {Johannes C.} and Mackay, {Clare E.} and Dirkx, {Michiel F.} and Rick Helmich and Francesca Assogna and Fabrizio Piras and Bright, {Joanna K.} and Gianfranco Spalletta and Kathleen Poston and Christine Lochner and McMillan, {Corey T.} and Daniel Weintraub and Jason Druzgal and Benjamin Newman and {Van Den Heuvel}, {Odile A.} and Neda Jahanshad and {van der Werf}, {Ysbrand D.} and Boris Gutman",
year = "2022",
month = oct,
day = "6",
doi = "10.1007/978-3-031-17899-3_12",
language = "English",
isbn = "9783031178986",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "115--124",
editor = "Ahmed Abdulkadir and Bathula, {Deepti R.} and Dvornek, {Nicha C.} and Mohamad Habes and Kia, {Seyed Mostafa} and Vinod Kumar and Thomas Wolfers",
booktitle = "Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings",
address = "Germany",
note = "5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",
}
RIS
TY - GEN
T1 - Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging
AU - ENIGMA consortium
AU - Zhao, Yuji
AU - Laansma, Max A.
AU - van Heese, Eva M.
AU - Owens-Walton, Conor
AU - Parkes, Laura M.
AU - Debove, Ines
AU - Rummel, Christian
AU - Wiest, Roland
AU - Cendes, Fernando
AU - Guimaraes, Rachel
AU - Yasuda, Clarissa Lin
AU - Wang, Jiun Jie
AU - Anderson, Tim J.
AU - Dalrymple-Alford, John C.
AU - Melzer, Tracy R.
AU - Pitcher, Toni L.
AU - Schmidt, Reinhold
AU - Schwingenschuh, Petra
AU - Garraux, Gäetan
AU - Rango, Mario
AU - Squarcina, Letizia
AU - Al-Bachari, Sarah
AU - Emsley, Hedley C.A.
AU - Klein, Johannes C.
AU - Mackay, Clare E.
AU - Dirkx, Michiel F.
AU - Helmich, Rick
AU - Assogna, Francesca
AU - Piras, Fabrizio
AU - Bright, Joanna K.
AU - Spalletta, Gianfranco
AU - Poston, Kathleen
AU - Lochner, Christine
AU - McMillan, Corey T.
AU - Weintraub, Daniel
AU - Druzgal, Jason
AU - Newman, Benjamin
AU - Van Den Heuvel, Odile A.
AU - Jahanshad, Neda
AU - van der Werf, Ysbrand D.
AU - Gutman, Boris
PY - 2022/10/6
Y1 - 2022/10/6
N2 - We extend the sparse, spatially piecewise-contiguous linear classification framework for mesh-based data to ordinal logistic regression. The algorithm is intended for use with subcortical shape and cortical thickness data where progressive clinical staging is available, as is generally the case in neurodegenerative diseases. We apply the tool to Parkinson’s and Alzheimer’s disease staging. The resulting biomarkers predict Hoehn-Yahr and cognitive impairment stages at competitive accuracy; the models remain parsimonious and outperform one-against-all models in terms of the Akaike and Bayesian information criteria.
AB - We extend the sparse, spatially piecewise-contiguous linear classification framework for mesh-based data to ordinal logistic regression. The algorithm is intended for use with subcortical shape and cortical thickness data where progressive clinical staging is available, as is generally the case in neurodegenerative diseases. We apply the tool to Parkinson’s and Alzheimer’s disease staging. The resulting biomarkers predict Hoehn-Yahr and cognitive impairment stages at competitive accuracy; the models remain parsimonious and outperform one-against-all models in terms of the Akaike and Bayesian information criteria.
KW - Imaging biomarker
KW - Neurodegenerative Disease
KW - Ordinal regression
U2 - 10.1007/978-3-031-17899-3_12
DO - 10.1007/978-3-031-17899-3_12
M3 - Conference contribution/Paper
AN - SCOPUS:85141746165
SN - 9783031178986
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 124
BT - Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Abdulkadir, Ahmed
A2 - Bathula, Deepti R.
A2 - Dvornek, Nicha C.
A2 - Habes, Mohamad
A2 - Kia, Seyed Mostafa
A2 - Kumar, Vinod
A2 - Wolfers, Thomas
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 18 September 2022
ER -
