TY - GEN
T1 - Dynamic routing capsule networks for mild cognitive impairment diagnosis
AU - Jiao, Zhicheng
AU - Huang, Pu
AU - Kam, Tae Eui
AU - Hsu, Li Ming
AU - Wu, Ye
AU - Zhang, Han
AU - Shen, Dinggang
N1 - Funding Information:
Acknowledgement. This work was supported in part by NIH grants EB022880, AG053867, AG041721, AG049371 and AG042599.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Alzheimer’s disease (AD) is a chronic neurodegenerative disease that could cause severe cognitive damage to the patients. Diagnosis of AD at its preclinical stage, i.e., mild cognitive impairment (MCI), could help to prevent or slow down AD progression. With machine learning, automatic MCI diagnosis could be achieved. Most of the previous studies mainly share a similar frame-work, i.e., building a classifier based on the features extracted from static or dynamic functional connectivity. Recently, inspired by the great successes achieved by deep learning in other areas of medical image analysis, researchers have introduced neural network models for MCI diagnosis. In this paper, we propose dynamic routing capsule networks for MCI diagnosis. Our proposed methods are based on a novel neural network fashion of capsule net. Two variants of capsule net are designed and discussed, which respectively uses the intra-ROIs and inter-ROIs dynamic routing to obtain functional representation. More importantly, we design a learnable dynamic functional connectivity metric in our inter-ROIs dynamic model, in which the functional connectivity is dynamically learned during network training. To the best of our knowledge, it’s the first time to propose dynamic routing capsule networks for MCI diagnosis. Compared with other machine learning methods and deep learning model, our method can achieve superior performance from various aspects of evaluations.
AB - Alzheimer’s disease (AD) is a chronic neurodegenerative disease that could cause severe cognitive damage to the patients. Diagnosis of AD at its preclinical stage, i.e., mild cognitive impairment (MCI), could help to prevent or slow down AD progression. With machine learning, automatic MCI diagnosis could be achieved. Most of the previous studies mainly share a similar frame-work, i.e., building a classifier based on the features extracted from static or dynamic functional connectivity. Recently, inspired by the great successes achieved by deep learning in other areas of medical image analysis, researchers have introduced neural network models for MCI diagnosis. In this paper, we propose dynamic routing capsule networks for MCI diagnosis. Our proposed methods are based on a novel neural network fashion of capsule net. Two variants of capsule net are designed and discussed, which respectively uses the intra-ROIs and inter-ROIs dynamic routing to obtain functional representation. More importantly, we design a learnable dynamic functional connectivity metric in our inter-ROIs dynamic model, in which the functional connectivity is dynamically learned during network training. To the best of our knowledge, it’s the first time to propose dynamic routing capsule networks for MCI diagnosis. Compared with other machine learning methods and deep learning model, our method can achieve superior performance from various aspects of evaluations.
KW - Alzheimer’s disease
KW - Capsule networks
KW - Computer-aided diagnosis
KW - Deep learning
KW - Mild cognitive impairment
UR - http://www.scopus.com/inward/record.url?scp=85075641558&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_68
DO - 10.1007/978-3-030-32251-9_68
M3 - Conference contribution
AN - SCOPUS:85075641558
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 620
EP - 628
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 -