Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging
Contributo in Atti di convegno
Data di Pubblicazione:
2022
Citazione:
Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging / Y. Zhao, M.A. Laansma, E.M. van Heese, C. Owens-Walton, L.M. Parkes, I. Debove, C. Rummel, R. Wiest, F. Cendes, R. Guimaraes, C.L. Yasuda, J.-. Wang, T.J. Anderson, J.C. Dalrymple-Alford, T.R. Melzer, T.L. Pitcher, R. Schmidt, P. Schwingenschuh, G. Garraux, M. Rango, L. Squarcina, S. Al-Bachari, H.C.A. Emsley, J.C. Klein, C.E. Mackay, M.F. Dirkx, R. Helmich, F. Assogna, F. Piras, J.K. Bright, G. Spalletta, K. Poston, C. Lochner, C.T. Mcmillan, D. Weintraub, J. Druzgal, B. Newman, O.A. Van Den Heuvel, N. Jahanshad, P.M. Thompson, Y.D. van der Werf, B. Gutman (LECTURE NOTES IN COMPUTER SCIENCE). - In: Machine Learning in Clinical Neuroimaging / [a cura di] A. Abdulkadir, D.R. Bathula, N.C. Dvornek, M. Habes, S. Mostafa Kia, V. Kumar, T. Wolfers. - [s.l] : Springer Science and Business Media Deutschland GmbH, 2022. - ISBN 978-3-031-17898-6. - pp. 115-124 (( convegno 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022 tenutosi a Singapore nel 2022 [10.1007/978-3-031-17899-3_12].
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’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.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Imaging biomarker; Neurodegenerative Disease; Ordinal regression
Elenco autori:
Y. Zhao, M.A. Laansma, E.M. van Heese, C. Owens-Walton, L.M. Parkes, I. Debove, C. Rummel, R. Wiest, F. Cendes, R. Guimaraes, C.L. Yasuda, J.-. Wang, T.J. Anderson, J.C. Dalrymple-Alford, T.R. Melzer, T.L. Pitcher, R. Schmidt, P. Schwingenschuh, G. Garraux, M. Rango, L. Squarcina, S. Al-Bachari, H.C.A. Emsley, J.C. Klein, C.E. Mackay, M.F. Dirkx, R. Helmich, F. Assogna, F. Piras, J.K. Bright, G. Spalletta, K. Poston, C. Lochner, C.T. Mcmillan, D. Weintraub, J. Druzgal, B. Newman, O.A. Van Den Heuvel, N. Jahanshad, P.M. Thompson, Y.D. van der Werf, B. Gutman
Link alla scheda completa:
Titolo del libro:
Machine Learning in Clinical Neuroimaging