How big data and high-performance computing drive brain science
S Chen, Z He, X Han, X He, R Li, H Zhu… - Genomics …, 2019 - academic.oup.com
Brain science accelerates the study of intelligence and behavior, contributes fundamental
insights into human cognition, and offers prospective treatments for brain disease. Faced …
insights into human cognition, and offers prospective treatments for brain disease. Faced …
A review on community detection in large complex networks from conventional to deep learning methods: a call for the use of parallel meta-heuristic algorithms
Complex networks (CNs) have gained much attention in recent years due to their
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …
Deep spatial-temporal feature fusion from adaptive dynamic functional connectivity for MCI identification
Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic
Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic …
Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic …
Deep learning for heterogeneous medical data analysis
At present, how to make use of massive medical information resources to provide scientific
decision-making for the diagnosis and treatment of diseases, summarize the curative effect …
decision-making for the diagnosis and treatment of diseases, summarize the curative effect …
Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
In this paper, a parallel deep learning-based community detection method in large complex
networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN …
networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN …
[HTML][HTML] Representation learning of resting state fMRI with variational autoencoder
Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but
structured patterns. However, the underlying origins are unclear and entangled in rsfMRI …
structured patterns. However, the underlying origins are unclear and entangled in rsfMRI …
Obstetric imaging diagnostic platform based on cloud computing technology under the background of smart medical big data and deep learning
W Lie, B Jiang, W Zhao - IEEE Access, 2020 - ieeexplore.ieee.org
The deep learning methods in the field of computer vision and big data are becoming more
and more mature. Through the application of big data and deep learning technology, the …
and more mature. Through the application of big data and deep learning technology, the …
Improving variational autoencoder with deep feature consistent and generative adversarial training
We present a new method for improving the performances of variational autoencoder (VAE).
In addition to enforcing the deep feature consistent principle thus ensuring the VAE output …
In addition to enforcing the deep feature consistent principle thus ensuring the VAE output …
Dimensionality reduction methods for brain imaging data analysis
The past century has witnessed the grand success of brain imaging technologies, such as
electroencephalography and magnetic resonance imaging, in probing cognitive states and …
electroencephalography and magnetic resonance imaging, in probing cognitive states and …
Changing the nature of quantitative biology education: Data science as a driver
We live in a data-rich world with rapidly growing databases with zettabytes of data.
Innovation, computation, and technological advances have now tremendously accelerated …
Innovation, computation, and technological advances have now tremendously accelerated …