Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 6/10/2022 |
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Host publication | Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings |
Editors | Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Thomas Wolfers |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 115-124 |
Number of pages | 10 |
ISBN (electronic) | 9783031178993 |
ISBN (print) | 9783031178986 |
<mark>Original language</mark> | English |
Event | 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 Duration: 18/09/2022 → 18/09/2022 |
Conference | 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 |
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Country/Territory | Singapore |
City | Singapore |
Period | 18/09/22 → 18/09/22 |
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13596 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (electronic) | 1611-3349 |
Conference | 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 |
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Country/Territory | Singapore |
City | Singapore |
Period | 18/09/22 → 18/09/22 |
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.