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Deep Semantic Architecture with discriminative feature visualization for neuroimage analysis
[article]
2018
arXiv
pre-print
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Our results demonstrate the feasibility of using deep network architectures for neuroimaging analysis in different contexts such as, for the identification of robust brain biomarkers to better characterize ...
Neuroimaging data analysis often involves a-priori selection of data features to study the underlying neural activity. ...
Deep Semantic Architecture with discriminative
feature visualization for neuroimage analysis
Arna Ghosh
Integrated Program in Neuroscience
McGill University
Montréal, QC H3A 0G4. ...
arXiv:1805.11704v2
fatcat:ikpqxjp2mbahflj5ohr6ms23ea
Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway
2018
Scientific Reports
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In the combined model, concept activation is driven by visual input and co-occurrence of semantic features, consistent with neurocognitive accounts. ...
Here we investigate visuo-semantic processing by combining a deep neural network model of vision with an attractor network model of semantics, such that visual information maps onto object meanings represented ...
For each visual node, we display 12 of the 16 images with the highest values for that node. Also shown for each node are other semantic features with connection weights > 10. ...
doi:10.1038/s41598-018-28865-1
pmid:30006530
pmcid:PMC6045572
fatcat:ck5baujmbnfg3asf362chlx5em
Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway
[article]
2018
bioRxiv
pre-print
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In the combined model, concept activation is driven by visual input and co-occurrence of semantic features, consistent with neurocognitive accounts. ...
Here we investigate visuo-semantic processing by combining a deep neural network model of vision with an attractor network model of semantics, such that visual information maps onto object meanings represented ...
For each visual node, we display the 16 images with the highest values for that node. Also shown for each node are other semantic features with connection weights > 10. ...
doi:10.1101/302406
fatcat:vchi2nfum5ehxotgfuyu7vtfl4
Brain2Image
2017
Proceedings of the 2017 ACM on Multimedia Conference - MM '17
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Leveraging on these recent trends, in this paper we present an approach for generating images using visually-evoked brain signals recorded through an electroencephalograph (EEG). ...
The obtained performance provides useful hints on the fact that EEG contains patterns related to visual content and that such patterns can be used to effectively generate images that are semantically coherent ...
Martina Platania for carrying out the EEG data acquisition. ...
doi:10.1145/3123266.3127907
dblp:conf/mm/KavasidisPSGS17
fatcat:dkn37i3dwfehvhrt4nmt3y2xiq
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
2018
AMIA Annual Symposium Proceedings
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We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. ...
Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained ...
Architecture of Deep 3D Convolutional Neural Networks The architecture of the deep 3D convolutional neural networks (3D-CNN) for Alzheimer's disease classification in this study are based on the network ...
pmid:30815203
pmcid:PMC6371279
fatcat:3tyqplujlnfqpdahs5ofk4irhq
A Survey on Deep Learning for Multimodal Data Fusion
2020
Neural Computation
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Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method ...
Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. ...
features with additional discriminative label information. ...
doi:10.1162/neco_a_01273
pmid:32186998
fatcat:ls27tbkldrbx7n4h7nlc73qyte
Unfolding the Effects of Acute Cardiovascular Exercise on Neural Correlates of Motor Learning Using Convolutional Neural Networks
2019
Frontiers in Neuroscience
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Our study thus demonstrates the feasibility of using deep network architectures for neuroimaging analysis, even in small-scale studies, to identify robust brain biomarkers and investigate neuroscience-based ...
selected features from neuroimaging data, including EEG. ...
We would also like to thank Alba Xifra-Porxas for helpful comments on the analysis pipeline and the presented figures. ...
doi:10.3389/fnins.2019.01215
pmid:31798403
pmcid:PMC6868001
fatcat:v2qaeuosyzeyportnz5pye7rmy
A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD)
[article]
2020
arXiv
pre-print
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T1-weighted MRI scans, we have done experiment with state of the art deep learning architecture i.e. VGG16 . ...
Finally, for benchmarking and to verify potential of deep learning on analyzing neuroimaging data i.e. ...
Finally, to verify potential of deep learning (LeCun et al. 2015) on analyzing neuroimaging data, we have done experiment with state of the art deep learning architecture i.e. ...
arXiv:1903.11323v3
fatcat:fj2wnm54mja2diybzple6exl4q
fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey
2022
Brain Sciences
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With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic ...
Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain–computer interface (BCI). ...
The shape decoder reconstructs the contour of the visual image from the lower visual cortex (V1, V2, V3), and the semantic decoder is responsible for extracting the semantic features of the visual image ...
doi:10.3390/brainsci12020228
pmid:35203991
pmcid:PMC8869956
fatcat:t664eccq6nh5plnvhac2r2gcpa
Generative adversarial networks for reconstructing natural images from brain activity
2018
NeuroImage
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We explore a straightforward method for reconstructing visual stimuli from brain activity. ...
Using this approach we were able to reconstruct natural images, but not to an equal extent for all images with the same model. ...
Acknowledgements We would like to express our gratitude to Kendrick Kay, who provided us with an updated vim-1 dataset, including data for a third subject. ...
doi:10.1016/j.neuroimage.2018.07.043
pmid:30031932
fatcat:labmhjs5obdt3n45wuhsctjepe
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation
[article]
2021
arXiv
pre-print
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The results do highlight that model-driven classical statistics and data-driven deep learning is a potent combination for developing automated systems in clinical oncology. ...
In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation. ...
semantic features. ...
arXiv:2103.05529v1
fatcat:iqu5ix5tgre6pnokdmoejywh74
Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex
[article]
2019
bioRxiv
pre-print
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Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. ...
The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. ...
shape or category using Linear Discriminant Analysis (LDA). ...
doi:10.1101/555193
fatcat:wvoht36gf5hbtpmhtt6c5vkn3y
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
[article]
2020
arXiv
pre-print
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First, we adopt a Convolutional Neural Network (CNN) to learn highly informative latent representation for the raw EEG signals, which is vital for the subsequent shape reconstruction. ...
Recently, the advancements of deep generative models like Generative Adversarial Networks (GANs) have supported the object generation from neurological signals. ...
to recover the geometric shapes which are visualizing by the human. ...
arXiv:1907.13351v2
fatcat:ilacd7mqazfqvcyb2v6pcdvbda
Generative adversarial networks for reconstructing natural images from brain activity
[article]
2017
bioRxiv
pre-print
Preserved Fulltext
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We explore a method for reconstructing visual stimuli from brain activity. ...
Using this approach we were able to reconstruct structural and some semantic features of a proportion of the natural images sets. ...
We used layers conv1 and conv2 for feature matching as these represent universal low-level features and simple patterns in the AlexNet architecture. ...
doi:10.1101/226688
fatcat:g6qpcbg6ubhlbgkur27qzkdtzq
Adapting fisher vectors for histopathology image classification
2017
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
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High-level feature based PET image retrieval with deep learning architecture. The Journal of Nuclear Medicine, 55.
2014). ...
Semantic association for neuroimaging classification of PET images. The Journal of Nuclear Medicine, 55.
. Dictionary pruning with visual word significance for medical image retrieval. ...
doi:10.1109/isbi.2017.7950592
dblp:conf/isbi/SongZCC17
fatcat:uta2mxug2ffapob22uixgoq6ai
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