TY - GEN
T1 - End-to-end dementia status prediction from brain MRI using multi-task weakly-supervised attention network
AU - Lian, Chunfeng
AU - Liu, Mingxia
AU - Wang, Li
AU - Shen, Dinggang
N1 - Funding Information:
Acknowledgements. This work was supported in part by NIH grants (EB008374, AG041721, AG042599, and EB022880).
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Computer-aided prediction of dementia status (e.g., clinical scores of cognitive tests) from brain MRI is of great clinical value, as it can help assess pathological stage and predict disease progression. Existing learning-based approaches typically preselect dementia-sensitive regions from the whole-brain MRI for feature extraction and prediction model construction, which might be sub-optimal due to potential heterogeneities between different steps. Also, based on anatomical prior knowledge (e.g., brain atlas) and time-consuming nonlinear registration, these preselected brain regions are usually the same across all subjects, ignoring their individual specificities in dementia progression. In this paper, we propose a multi-task weakly-supervised attention network (MWAN) to jointly predict multiple clinical scores from the baseline MRI data, by explicitly considering individual specificities of different subjects. Leveraging a fully-trainable dementia attention block, our MWAN method can automatically identify subject-specific discriminative locations from the whole-brain MRI for end-to-end feature learning and multi-task regression. We evaluated our MWAN method by cross-validation on two public datasets (i.e., ADNI-1 and ADNI-2). Experimental results demonstrate that the proposed method performs well in both the tasks of clinical score prediction and weakly-supervised discriminative localization in brain MR images.
AB - Computer-aided prediction of dementia status (e.g., clinical scores of cognitive tests) from brain MRI is of great clinical value, as it can help assess pathological stage and predict disease progression. Existing learning-based approaches typically preselect dementia-sensitive regions from the whole-brain MRI for feature extraction and prediction model construction, which might be sub-optimal due to potential heterogeneities between different steps. Also, based on anatomical prior knowledge (e.g., brain atlas) and time-consuming nonlinear registration, these preselected brain regions are usually the same across all subjects, ignoring their individual specificities in dementia progression. In this paper, we propose a multi-task weakly-supervised attention network (MWAN) to jointly predict multiple clinical scores from the baseline MRI data, by explicitly considering individual specificities of different subjects. Leveraging a fully-trainable dementia attention block, our MWAN method can automatically identify subject-specific discriminative locations from the whole-brain MRI for end-to-end feature learning and multi-task regression. We evaluated our MWAN method by cross-validation on two public datasets (i.e., ADNI-1 and ADNI-2). Experimental results demonstrate that the proposed method performs well in both the tasks of clinical score prediction and weakly-supervised discriminative localization in brain MR images.
UR - http://www.scopus.com/inward/record.url?scp=85075656886&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_18
DO - 10.1007/978-3-030-32251-9_18
M3 - Conference contribution
AN - SCOPUS:85075656886
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 167
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 -