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Title: Dual-Attention Recurrent Networks for Affine Registration of Neuroimaging Data
Neuroimaging data typically undergoes several preprocessing steps before further analysis and mining can be done. Affine image registration is one of the important tasks during preprocessing. Recently, several image registration methods which are based on Convolutional Neural Networks have been proposed. However, due to the high computational and memory requirements of CNNs, these methods cannot be used in real-time for large neuroimaging data like fMRI. In this paper, we propose a Dual-Attention Recurrent Network (DRN) which uses a hard attention mechanism to allow the model to focus on small, but task-relevant, parts of the input image – thus reducing computational and memory costs. Furthermore, DRN naturally supports inhomogeneity between the raw input image (e.g., functional MRI) and the image we want to align it to (e.g., anatomical MRI) so it can be applied to harder registration tasks such as fMRI coregistration and normalization. Extensive experiments on two different datasets demonstrate that DRN significantly reduces the computational and memory costs compared with other neural network-based methods without sacrificing the quality of image registration
Authors:
; ; ; ;
Award ID(s):
1718310
Publication Date:
NSF-PAR ID:
10215776
Journal Name:
Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
Page Range or eLocation-ID:
379-387
Sponsoring Org:
National Science Foundation
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