Recent advances in explainable artificial intelligence for magnetic resonance imaging
Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated
magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image …
magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image …
An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders
It has been proven that neuropsychiatric disorders (NDs) can be associated with both
structures and functions of brain regions. Thus, data about structures and functions could be …
structures and functions of brain regions. Thus, data about structures and functions could be …
Deep learning in neuroimaging: Promises and challenges
Deep learning (DL) has been extremely successful when applied to the analysis of natural
images. By contrast, analyzing neuroimaging data presents some unique challenges …
images. By contrast, analyzing neuroimaging data presents some unique challenges …
Multimodal Fusion of Brain Imaging Data: Methods and Applications
Neuroimaging data typically include multiple modalities, such as structural or functional
magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography …
magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography …
Dynamic weighted hypergraph convolutional network for brain functional connectome analysis
The hypergraph structure has been utilized to characterize the brain functional connectome
(FC) by capturing the high order relationships among multiple brain regions of interest …
(FC) by capturing the high order relationships among multiple brain regions of interest …
Interpretable cognitive ability prediction: A comprehensive gated graph transformer framework for analyzing functional brain networks
Graph convolutional deep learning has emerged as a promising method to explore the
functional organization of the human brain in neuroscience research. This paper presents a …
functional organization of the human brain in neuroscience research. This paper presents a …
Brain functional connectivity analysis via graphical deep learning
Objective: Graphical deep learning models provide a desirable way for brain functional
connectivity analysis. However, the application of current graph deep learning models to …
connectivity analysis. However, the application of current graph deep learning models to …
Multi-modal imaging genetics data fusion via a hypergraph-based manifold regularization: Application to schizophrenia study
Recent studies show that multi-modal data fusion techniques combine information from
diverse sources for comprehensive diagnosis and prognosis of complex brain disorder …
diverse sources for comprehensive diagnosis and prognosis of complex brain disorder …
Tackling the accuracy-interpretability trade-off: Interpretable deep learning models for satellite image-based real estate appraisal
JP Kucklick, O Müller - ACM Transactions on Management Information …, 2023 - dl.acm.org
Deep learning models fuel many modern decision support systems, because they typically
provide high predictive performance. Among other domains, deep learning is used in real …
provide high predictive performance. Among other domains, deep learning is used in real …
Latent similarity identifies important functional connections for phenotype prediction
Objective: Endophenotypes such as brain age and fluid intelligence are important
biomarkers of disease status. However, brain imaging studies to identify these biomarkers …
biomarkers of disease status. However, brain imaging studies to identify these biomarkers …