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Conference Paper: Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization

TitlePredicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization
Authors
Issue Date2019
PublisherSpringer.
Citation
First Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction (ABCD-NP 2019), Held in Conjunction with MICCAI 2019, Shenzhen, China, 13 October 2019. In Pohl, KM, Thompson, WK, Adeli, E, Linguraru, MG (Eds.), Adolescent Brain Cognitive Development Neurocognitive Prediction: First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings, p. 108-113. Cham, Switzerland: Springer, 2019 How to Cite?
AbstractIn this paper, we develop a 3D convolutional neural network to predict the fluid intelligence from T1-weighted MRI images by adding an encoder-decoder regularization. Considering that cerebellar volume is often highly correlated to intelligence of an individual, we propose to incorporate this morphological information into the framework for fluid intelligence prediction by utilizing an encoder-decoder regularization for brain structure segmentation simultaneously. Specifically, we first train an encoder-decoder network to generate the brain segmentation mask, where the discriminative morphological feature of the brain volume can be learned. Then, we reuse the encoder path of the network as the prediction network backbone for final fluid intelligence prediction by adding an additional regression part to predict the fluid intelligence value. The proposed framework is able to learn the discriminative relationship between the morphological information of brain structures and the intelligence score for more accurate prediction.
Persistent Identifierhttp://hdl.handle.net/10722/299613
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 11791

 

DC FieldValueLanguage
dc.contributor.authorLiu, Lihao-
dc.contributor.authorYu, Lequan-
dc.contributor.authorWang, Shujun-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:47Z-
dc.date.available2021-05-21T03:34:47Z-
dc.date.issued2019-
dc.identifier.citationFirst Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction (ABCD-NP 2019), Held in Conjunction with MICCAI 2019, Shenzhen, China, 13 October 2019. In Pohl, KM, Thompson, WK, Adeli, E, Linguraru, MG (Eds.), Adolescent Brain Cognitive Development Neurocognitive Prediction: First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings, p. 108-113. Cham, Switzerland: Springer, 2019-
dc.identifier.isbn9783030319007-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299613-
dc.description.abstractIn this paper, we develop a 3D convolutional neural network to predict the fluid intelligence from T1-weighted MRI images by adding an encoder-decoder regularization. Considering that cerebellar volume is often highly correlated to intelligence of an individual, we propose to incorporate this morphological information into the framework for fluid intelligence prediction by utilizing an encoder-decoder regularization for brain structure segmentation simultaneously. Specifically, we first train an encoder-decoder network to generate the brain segmentation mask, where the discriminative morphological feature of the brain volume can be learned. Then, we reuse the encoder path of the network as the prediction network backbone for final fluid intelligence prediction by adding an additional regression part to predict the fluid intelligence value. The proposed framework is able to learn the discriminative relationship between the morphological information of brain structures and the intelligence score for more accurate prediction.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdolescent Brain Cognitive Development Neurocognitive Prediction: First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 11791-
dc.titlePredicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-030-31901-4_13-
dc.identifier.scopuseid_2-s2.0-85075662794-
dc.identifier.spage108-
dc.identifier.epage113-
dc.identifier.eissn1611-3349-
dc.publisher.placeCham, Switzerland-

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