Restricted Boltzmann Machines for Neuroimaging: an Application in Identifying Intrinsic Networks
R Devon Hjelm,Vince D. Calhoun,Ruslan Salakhutdinov,Elena A. Allen,Tulay Adali,Sergey M. Plis +5 more
TLDR
It is shown that RBMs can be used to identify networks and their temporal activations with accuracy that is equal or greater than that of factorization models, a significant prospect for future neuroimaging research.About:
This article is published in NeuroImage.The article was published on 2014-08-01 and is currently open access. It has received 144 citations till now. The article focuses on the topics: Restricted Boltzmann machine & Boltzmann machine.read more
Citations
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Journal ArticleDOI
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.
TL;DR: A novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning that could hierarchically discover the complex latent patterns inherent in both MRI and PET.
Journal ArticleDOI
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
Journal ArticleDOI
Identification of autism spectrum disorder using deep learning and the ABIDE dataset.
Anibal Sólon Heinsfeld,Alexandre Rosa Franco,R. Cameron Craddock,Augusto Buchweitz,Felipe Meneguzzi,Felipe Meneguzzi +5 more
TL;DR: The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset, and identified the areas of the brain that contributed most to differentiating ASD from typically developing controls as per the deep learning model.
Journal ArticleDOI
Deep learning for neuroimaging: A validation study
Sergey M. Plis,R Devon Hjelm,Ruslan Salakhutdinov,Elena A. Allen,Elena A. Allen,HJ Bockholt,Jeffrey D. Long,Jeffrey D. Long,Hans J. Johnson,Hans J. Johnson,Jane S. Paulsen,Jane S. Paulsen,Jessica A. Turner,Vince D. Calhoun,Vince D. Calhoun +14 more
TL;DR: In this article, a constraint-based approach to visualizing high dimensional data was proposed to analyze the effect of parameter choices on data transformations and showed that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
Proceedings Article
Deep learning for neuroimaging: A validation study
TL;DR: In this article, a constraint-based approach to visualizing high dimensional data is proposed to analyze the effect of parameter choices on data transformations and show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
References
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ImageNet Classification with Deep Convolutional Neural Networks
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A fast learning algorithm for deep belief nets
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Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
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An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.