TY - GEN
T1 - Deep Low-Rank Multimodal Fusion with Inter-modal Distribution Difference Constraint for ASD Diagnosis
AU - Xue, Minhao
AU - Wang, Li
AU - Shen, Jie
AU - Wang, Kangning
AU - Wu, Wanning
AU - Fu, Long
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by complex symptoms, which makes ASD difficult to be identified. Combining different brain imaging modalities to provide complementary information has been extensively used in the diagnosis of brain disorders. However, it is still very difficult to fully integrate different modalities by capturing the complex connections between different modalities. To solve this problem, we propose a deep low-rank multimodal fusion (DLMF) network that takes into account distribution discrepancy between different modalities. This network aims to learn the complex connections between rest-state functional magnetic resonance imaging (rs-fMRI) and structural magnetic resonance imaging (sMRI) in order to effectively perform multimodal identification of Autism Spectrum Disorder (ASD). Firstly, two different networks are used to extract the features that represent complex information in the rs-fMRI and sMRI data. Then, a measurement function is proposed to quantify distribution discrepancy between different modalities. This measurement function is then incorporated into the loss function of our low-rank multimodal fusion network. Therefore, our method can reduce the distribution discrepancy between different modalities through joint learning from rs-fMRI and sMRI data. The classifier in our approach adopts Support Vector Machines (SVM). The proposed network was trained with the new loss function using an end-to-end training approach. We verify the effectiveness of our method on a publicly available multimodal dataset: ABIDE database. Experimental results show that our methods are superior to several of the most advanced ASD diagnostic methods currently available.
AB - Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by complex symptoms, which makes ASD difficult to be identified. Combining different brain imaging modalities to provide complementary information has been extensively used in the diagnosis of brain disorders. However, it is still very difficult to fully integrate different modalities by capturing the complex connections between different modalities. To solve this problem, we propose a deep low-rank multimodal fusion (DLMF) network that takes into account distribution discrepancy between different modalities. This network aims to learn the complex connections between rest-state functional magnetic resonance imaging (rs-fMRI) and structural magnetic resonance imaging (sMRI) in order to effectively perform multimodal identification of Autism Spectrum Disorder (ASD). Firstly, two different networks are used to extract the features that represent complex information in the rs-fMRI and sMRI data. Then, a measurement function is proposed to quantify distribution discrepancy between different modalities. This measurement function is then incorporated into the loss function of our low-rank multimodal fusion network. Therefore, our method can reduce the distribution discrepancy between different modalities through joint learning from rs-fMRI and sMRI data. The classifier in our approach adopts Support Vector Machines (SVM). The proposed network was trained with the new loss function using an end-to-end training approach. We verify the effectiveness of our method on a publicly available multimodal dataset: ABIDE database. Experimental results show that our methods are superior to several of the most advanced ASD diagnostic methods currently available.
KW - Autism Spectrum Disorder
KW - Low-rank Multimodal Fusion
KW - distribution discrepancy
UR - http://www.scopus.com/inward/record.url?scp=85177432789&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-46317-4_10
DO - 10.1007/978-3-031-46317-4_10
M3 - 会议稿件
AN - SCOPUS:85177432789
SN - 9783031463167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 106
EP - 115
BT - Image and Graphics - 12th International Conference, ICIG 2023, Proceedings
A2 - Lu, Huchuan
A2 - Liu, Risheng
A2 - Ouyang, Wanli
A2 - Huang, Hui
A2 - Lu, Jiwen
A2 - Dong, Jing
A2 - Xu, Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Image and Graphics, ICIG 2023
Y2 - 22 September 2023 through 24 September 2023
ER -