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Deep Semantic Architecture with discriminative feature visualization for neuroimage analysis [article]

Arna Ghosh, Fabien dal Maso, Marc Roig, Georgios D Mitsis and Marie-Hélène Boudrias
2018 arXiv   pre-print
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

Barry J. Devereux, Alex Clarke, Lorraine K. Tyler
2018 Scientific Reports  
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]

Barry J. DEVEREUX, Alex D. Clarke, Lorraine K. Tyler
2018 bioRxiv   pre-print
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

Isaak Kavasidis, Simone Palazzo, Concetto Spampinato, Daniela Giordano, Mubarak Shah
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
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

Chengliang Yang, Anand Rangarajan, Sanjay Ranka
2018 AMIA Annual Symposium Proceedings  
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

Jing Gao, Peng Li, Zhikui Chen, Jianing Zhang
2020 Neural Computation  
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

Arna Ghosh, Fabien Dal Maso, Marc Roig, Georgios D. Mitsis, Marie-Hélène Boudrias
2019 Frontiers in Neuroscience  
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]

Hamza Sharif, Rizwan Ahmed Khan
2020 arXiv   pre-print
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

Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning
2022 Brain Sciences  
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

K. Seeliger, U. Güçlü, L. Ambrogioni, Y. Güçlütürk, M.A.J. van Gerven
2018 NeuroImage  
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]

K. Ruwani M. Fernando, Chris P. Tsokos
2021 arXiv   pre-print
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]

Astrid Zeman, J. Brendan Ritchie, Stefania Bracci, Hans Op de Beeck
2019 bioRxiv   pre-print
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]

Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu, Chang Ge, Lina Yao
2020 arXiv   pre-print
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]

K. Seeliger, U. Güçlü, L. Ambrogioni, Y. Güçlütürk, M. A. J. van Gerven
2017 bioRxiv   pre-print
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

Yang Song, Ju Jia Zou, Hang Chang, Weidong Cai
2017 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)  
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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