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The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Marinescu RV., Oxtoby NP., Young AL., Bron EE., Toga AW., Weiner MW., Barkhof F., Fox NC., Eshaghi A., Toni T., Salaterski M., Lunina V., Ansart M., Durrleman S., Lu P., Iddi S., Li D., Thompson WK., Donohue MC., Nahon A., Levy Y., Halbersberg D., Cohen M., Liao H., Li T., Yu K., Zhu H., Tamez-Peña JG., Ismail A., Wood T., Bravo HC., Nguyen M., Sun N., Feng J., Yeo BTT., Chen G., Qi K., Chen S., Qiu D., Buciuman I., Kelner A., Pop R., Rimocea D., Ghazi MM., Nielsen M., Ourselin S., Sørensen L., Venkatraghavan V., Liu K., Rabe C.

Original publication

DOI

10.59275/j.melba.2021-2dcc

Type

Journal article

Journal

MELBA journal

Publisher

Machine Learning for Biomedical Imaging

Publication Date

31/12/2021

Volume

1

Pages

1 - 60

Keywords

31 Biological Sciences, 4.1 Discovery and preclinical testing of markers and technologies, 42 Health Sciences, 4203 Health Services and Systems, Acquired Cognitive Impairment, Aging, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Bioengineering, Brain Disorders, Clinical Research, Clinical Trials and Supportive Activities, Dementia, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Neurodegenerative, Neurological, Neurosciences, Prevention