Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to mapping the structure and function of the brain …
offer a non-invasive approach to mapping the structure and function of the brain …
[HTML][HTML] A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …
A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions
Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …
analysis and modeling of intricate relationships present in data structured as graphs. In …
[HTML][HTML] A comprehensive survey of complex brain network representation
Recent years have shown great merits in utilizing neuroimaging data to understand brain
structural and functional changes, as well as its relationship to different neurodegenerative …
structural and functional changes, as well as its relationship to different neurodegenerative …
[HTML][HTML] Transformer with convolution and graph-node co-embedding: an accurate and interpretable vision backbone for predicting gene expressions from local …
Inferring gene expressions from histopathological images has long been a fascinating yet
challenging task, primarily due to the substantial disparities between the two modality …
challenging task, primarily due to the substantial disparities between the two modality …
Identifying ADHD‐Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network
Y Hu, J Ran, R Qiao, J Xu, C Tan, L Hu… - Neural Plasticity, 2024 - Wiley Online Library
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder
that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms …
that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms …
Predicting brain multigraph population from a single graph template for boosting one-shot classification
A central challenge in training one-shot learning models is the limited representativeness of
the available shots of the data space. Particularly in the field of network neuroscience where …
the available shots of the data space. Particularly in the field of network neuroscience where …
Inter-domain alignment for predicting high-resolution brain networks using teacher-student learning
Accurate and automated super-resolution image synthesis is highly desired since it has the
great potential to circumvent the need for acquiring high-cost medical scans and a time …
great potential to circumvent the need for acquiring high-cost medical scans and a time …
A Survey of GNN in Bioinformation Data
Z Zhu - 2022 - preprints.org
With the development of data science, more and more machine learning technologies have
been designed to solve complicated and challenging real-world tasks containing a large …
been designed to solve complicated and challenging real-world tasks containing a large …
Research on the Application of Graph Neural Networks in Financial Asset Valuation
Y Song, Z He - Proceedings of the 2023 International Conference on …, 2023 - dl.acm.org
To explore the effectiveness of graph neural networks in the assessment of financial asset
risk, this research constructs a stock relationship network using price correlation coefficients …
risk, this research constructs a stock relationship network using price correlation coefficients …