Modern views of machine learning for precision psychiatry
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …
the advent of functional neuroimaging, novel technologies and methods provide new …
Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used
for automated diagnosis of brain disorders such as major depressive disorder (MDD) to …
for automated diagnosis of brain disorders such as major depressive disorder (MDD) to …
Robust and sparse canonical correlation analysis for fault detection and diagnosis using training data with outliers
A well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack
of robustness against outliers. This shortcoming hinders the application of CCA in the case …
of robustness against outliers. This shortcoming hinders the application of CCA in the case …
Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases
T Wang, X Chen, J Zhang, Q Feng, M Huang - Medical Image Analysis, 2023 - Elsevier
Imaging genetics is a crucial tool that is applied to explore potentially disease-related
biomarkers, particularly for neurodegenerative diseases (NDs). With the development of …
biomarkers, particularly for neurodegenerative diseases (NDs). With the development of …
Robust statistical industrial fault monitoring: A machine learning-based distributed CCA and low frequency control charts
Over the past two decades, there has been a notable increase in the complexity and
dynamism of industrial and manufacturing systems. Traditional fault detection strategies …
dynamism of industrial and manufacturing systems. Traditional fault detection strategies …
Attentive deep canonical correlation analysis for diagnosing alzheimer's disease using multimodal imaging genetics
Integration of imaging genetics data provides unprecedented opportunities for revealing
biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new …
biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new …
Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease
Investigating the relationship between genetic variation and phenotypic traits is a key issue
in quantitative genetics. Specifically for Alzheimer's disease, the association between …
in quantitative genetics. Specifically for Alzheimer's disease, the association between …
Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network
Multi-modal data can provide complementary information of Alzheimer's disease (AD) and
its development from different perspectives. Such information is closely related to the …
its development from different perspectives. Such information is closely related to the …
Homogeneous-multiset-CCA-based brain covariation and contravariance connectivity network modeling
Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have
expanded our understanding of brain functions in both healthy and diseased states …
expanded our understanding of brain functions in both healthy and diseased states …
An automatic image Processing Method based on Artificial Intelligence for locating the key boundary points in the Central Serous Chorioretinopathy Lesion Area
J Xu, J Shen, C Wan, Z Yan, F Zhou… - Computational …, 2023 - Wiley Online Library
Accurately and rapidly measuring the diameter of central serous chorioretinopathy (CSCR)
lesion area is the key to judge the severity of CSCR and evaluate the efficacy of the …
lesion area is the key to judge the severity of CSCR and evaluate the efficacy of the …