@inproceedings{7256681ef6d64c31bf6b8cb16007c9a0,
title = "Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer{\textquoteright}s disease",
abstract = "Alzheimer{\textquoteright}s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.",
author = "Xiaoqian Wang and Dinggang Shen and Heng Huang",
year = "2016",
doi = "10.1007/978-3-319-46720-7_32",
language = "English",
isbn = "9783319467191",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "273--281",
editor = "Gozde Unal and Sabuncu, {Mert R.} and William Wells and Sebastian Ourselin and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
note = "1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",
}