Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics

Autor: Tianming Liu, Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
0209 industrial biotechnology
Computer science
Cognitive Neuroscience
Big data
Machine Learning (stat.ML)
02 engineering and technology
computer.software_genre
Blind signal separation
Convolutional neural network
Article
Machine Learning (cs.LG)
Data modeling
020901 industrial engineering & automation
Statistics - Machine Learning
Artificial Intelligence
0202 electrical engineering
electronic engineering
information engineering
Artificial Intelligence & Image Processing
Neural and Evolutionary Computing (cs.NE)
Artificial neural network
business.industry
Computer Science - Neural and Evolutionary Computing
Independent component analysis
Autoencoder
Computer Science Applications
Computer Science - Learning
Computer Science - Distributed
Parallel
and Cluster Computing
Analytics
FOS: Biological sciences
Quantitative Biology - Neurons and Cognition
Neurons and Cognition (q-bio.NC)
020201 artificial intelligence & image processing
Distributed
Parallel
and Cluster Computing (cs.DC)
Data mining
business
computer
Zdroj: University of Technology Sydney
ISSN: 0925-2312
Popis: In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying neural activities, modeling tfMRI data is hard and challenging. Previously proposed data-modeling methods including Independent Component Analysis (ICA) and Sparse Dictionary Learning only provided a weakly established model based on blind source separation under the strong assumption that original fMRI signals could be linearly decomposed into time series components with corresponding spatial maps. Meanwhile, analyzing and learning a large amount of tfMRI data from a variety of subjects has been shown to be very demanding but yet challenging even with technological advances in computational hardware. Given the Convolutional Neural Network (CNN), a robust method for learning high-level abstractions from low-level data such as tfMRI time series, in this work we propose a fast and scalable novel framework for distributed deep Convolutional Autoencoder model. This model aims to both learn the complex hierarchical structure of the tfMRI data and to leverage the processing power of multiple GPUs in a distributed fashion. To implement such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow library, leveraging from a very large cluster of GPU machines. Experimental data from applying the model on the Human Connectome Project (HCP) show that the proposed model is efficient and scalable toward tfMRI big data analytics, thus enabling data-driven extraction of hierarchical neuroscientific information from massive fMRI big data in the future.
This work is submitted to SIGKDD 2018
Databáze: OpenAIRE
Externí odkaz: