TY - GEN
T1 - Inter-modality dependence induced data recovery for MCI conversion prediction
AU - Zhou, Tao
AU - Thung, Kim Han
AU - Zhang, Yu
AU - Fu, Huazhu
AU - Shen, Jianbing
AU - Shen, Dinggang
AU - Shao, Ling
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Learning complementary information from multi-modality data often improves diagnostic performance of brain disorders. However, it is challenging to obtain this complementary information when the data are incomplete. Existing methods, such as low-rank matrix completion (which imputes the missing data) and multi-task learning (which restructures the problem into the joint learning of multiple tasks, with each task associated with a subset of complete data), simply concatenate features from different modalities without considering their underlying correlations. Furthermore, most methods conduct multi-modality fusion and prediction model learning in separated steps, which may render to a sub-optimal solution. To address these issues, we propose a novel diagnostic model that integrates missing data recovery, latent space learning and prediction model learning into a unified framework. Specifically, we first recover the missing modality by maximizing the dependency among different modalities. Then, we further exploit the modality correlation by projecting different modalities into a common latent space. Besides, we employ an l1 -norm to our loss function to mitigate the influence of sample outliers. Finally, we map the learned latent representation into the label space. All these tasks are learned iteratively in a unified framework, where the label information (from the training samples) can also inherently guide the missing modality recovery. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method.
AB - Learning complementary information from multi-modality data often improves diagnostic performance of brain disorders. However, it is challenging to obtain this complementary information when the data are incomplete. Existing methods, such as low-rank matrix completion (which imputes the missing data) and multi-task learning (which restructures the problem into the joint learning of multiple tasks, with each task associated with a subset of complete data), simply concatenate features from different modalities without considering their underlying correlations. Furthermore, most methods conduct multi-modality fusion and prediction model learning in separated steps, which may render to a sub-optimal solution. To address these issues, we propose a novel diagnostic model that integrates missing data recovery, latent space learning and prediction model learning into a unified framework. Specifically, we first recover the missing modality by maximizing the dependency among different modalities. Then, we further exploit the modality correlation by projecting different modalities into a common latent space. Besides, we employ an l1 -norm to our loss function to mitigate the influence of sample outliers. Finally, we map the learned latent representation into the label space. All these tasks are learned iteratively in a unified framework, where the label information (from the training samples) can also inherently guide the missing modality recovery. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=85075677931&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_21
DO - 10.1007/978-3-030-32251-9_21
M3 - Conference contribution
AN - SCOPUS:85075677931
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 195
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -