Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging-Reference-Cited by-同舟云学术

Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

Published:2022 Issue: Volume: Page:115-124
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Zhao Yuji,Laansma Max A.,van Heese Eva M.,Owens-Walton Conor,Parkes Laura M.,Debove Ines,Rummel Christian,Wiest Roland,Cendes Fernando,Guimaraes Rachel,Yasuda Clarissa Lin,Wang Jiun-Jie,Anderson Tim J.,Dalrymple-Alford John C.,Melzer Tracy R.,Pitcher Toni L.,Schmidt Reinhold,Schwingenschuh Petra,Garraux Gäetan,Rango Mario,Squarcina Letizia,Al-Bachari Sarah,Emsley Hedley C. A.,Klein Johannes C.,Mackay Clare E.,Dirkx Michiel F.,Helmich Rick,Assogna Francesca,Piras Fabrizio,Bright Joanna K.,Spalletta Gianfranco,Poston Kathleen,Lochner Christine,McMillan Corey T.,Weintraub Daniel,Druzgal Jason,Newman Benjamin,Van Den Heuvel Odile A.,Jahanshad Neda,Thompson Paul M.,van der Werf Ysbrand D.,Gutman Boris,

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-17899-3_12

Reference23 articles.

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3. Dohmatob, E.D., Gramfort,A., Thirion, B., Varoquaux, G.: Benchmarking solvers for tv-l1 least-squares and logistic regression in brain imaging. In: 2014 International Workshop on Pattern Recognition in Neuroimaging, pp. 1–4 (2014)

4. Doyle, O.M., et al.: Predicting progression of alzheimer’s disease using ordinal regression. PLoS ONE 9(8), e105542 (2014)

5. Lecture Notes in Computer Science;S Garbarino,2019

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