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

Yuji Zhao, Max A. Laansma, Eva M. van Heese, Conor Owens-Walton, Laura M. Parkes, Ines Debove, Christian Rummel, Roland Wiest, Fernando Cendes, Rachel Guimaraes, Clarissa Lin Yasuda, Jiun-Jie Wang, Tim J. Anderson, John C. Dalrymple-Alford, Tracy R. Melzer, Toni L. Pitcher, Reinhold Schmidt, Petra Schwingenschuh, Gaëtan Garraux, Mario Rango, Letizia Squarcina, Sarah Al-Bachari, Hedley C. A. Emsley, Johannes C. Klein, Clare E. Mackay, Michiel F. Dirkx, Rick C. Helmich, Francesca Assogna, Fabrizio Piras, Joanna K. Bright, Gianfranco Spalletta, Kathleen Poston, Christine Lochner, Corey T. McMillan, Daniel Weintraub, Jason Druzgal, Benjamin Newman, Odile A. van den Heuvel, Neda Jahanshad, Paul M. Thompson, Ysbrand D. van der Werf, Boris Gutman. Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Thomas Wolfers, editors, Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. Volume 13596 of Lecture Notes in Computer Science, pages 115-124, Springer, 2022. [doi]