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Found 32 papers, 13 papers with code
Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks
Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data.
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes.
A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder.
Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain.
Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort
Using the full FC and a training set of 2, 000 subjects, one is able to predict which scan is older 82. 5\% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.
Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.
LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference
We demonstrate the effectiveness of our approach and generate state-of-the-art results on different datasets.
Angle Basis: a Generative Model and Decomposition for Functional Connectivity
We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%.
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.
KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation
In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation.
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.
MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints
A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions.
A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms
We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.
Xiaomingbot: A Multilingual Robot News Reporter
This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation.
Point Set Voting for Partial Point Cloud Analysis
This paper illustrates that this proposed method achieves state-of-the-art performance on shape classification, part segmentation and point cloud completion.
Interpretable multimodal fusion networks reveal mechanisms of brain cognition
Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis.
Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
A popular definition of FC is by statistical associations between measured brain regions.
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Our network analysis revealed the development of emotion-related intra- and inter- modular connectivity and pinpointed several emotion-related hubs.
Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement
Firstly, the speech synthesis model is pre-trained with both multi-speaker clean data and noisy augmented data; then the pre-trained model is adapted on noisy low-resource new speaker data; finally, by setting the clean speech condition, the model can synthesize the new speaker's clean voice.
A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets
In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.
Multimodal Sparse Classifier for Adolescent Brain Age Prediction
The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood.
TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization
We show that by using TZC, the braking distance can be shortened by 16% than ROS.
Robotics
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.
FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics
Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR.
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.
Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis
Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.
Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis
Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets.
Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
Finally, we propose a method based on robust kernel CO and robust kernel CCO, called robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.
