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Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review [article]

Marjane Khodatars, Afshin Shoeibi, Navid Ghassemi, Mahboobeh Jafari, Ali Khadem, Delaram Sadeghi, Parisa Moridian, Sadiq Hussain, Roohallah Alizadehsani, Assef Zare, Abbas Khosravi, Saeid Nahavandi (+2 others)
2020 arXiv   pre-print
Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging.  ...  Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis.  ...  REGISTRATION TO A STANDARD ATLAS: The human brain entails hundreds of cortical and subcortical areas with various structures and functions, each of which is very timeconsuming and complex to study.  ... 
arXiv:2007.01285v3 fatcat:gtiblvspm5gn7kn2f7x4j474xm

COMPUTATIONAL MODELING OF DEMENTIA PREDICTION USING DEEP NEURAL NETWORK: ANALYSIS ON OASIS DATASET

Shakila Basheer, Surbhi Bhatia, Sapiah Sakri
2021 IEEE Access  
Acknowledgments: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research through project number "PNU-DRI-RI -20  ...  It starts from a specific subcortical region and increases to the cortical mantle with the passage of time. The most common effect of AD is memory loss and slows down the ability to do any task.  ...  A pre-trained 3D autoencoder is used to improve the accuracy as compared to state of art CNN model.  ... 
doi:10.1109/access.2021.3066213 fatcat:yz6uz5mpwbdoxlstnc7pur74cm

Building Machines That Learn and Think Like People [article]

Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman
2016 arXiv   pre-print
Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some  ...  to new tasks and situations.  ...  Acknowledgments We are grateful to Peter Battaglia, Matt Botvinick, Y-Lan Boureau, Shimon Edelman, Nando de Freitas, Anatole Gershman, George Kachergis, Leslie Kaelbling, Andrej Karpathy, George Konidaris  ... 
arXiv:1604.00289v3 fatcat:ph2rrwk2znb4dpb5nvcg54x2xi

Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework

Mingliang Li, Mingfeng Jiang, Guangming Zhang, Yujun Liu, Xiaobo Zhou
2022
The fluid intelligence score prediction results of the proposed method are found to be superior to those of current state-of-the-art approaches, and the proposed method achieves a mean square error (MSE  ...  In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to  ...  Moreover, the subcortical regions of subjects have been segmented by FSL FIRST; these regions were mainly cortical and did not include any subcortical regions of interest (ROIs) [7] .  ... 
doi:10.1371/journal.pone.0268707 pmid:35917308 fatcat:na4e5zjbdfc7rbazwxppwvwcfa

How Can We Reduce the Gulf between Artificial and Natural Intelligence?

Aaron Sloman
2014 International Workshop on Artificial Intelligence and Cognition  
www.di.unito.it) Progetto di Ateneo-San Paolo, The role of visual imagery in lexical processing (RVILP), TO call03 2012 0046.  ...  www.fondazionerosselli.it) Supporting Institutions Associazione Italiana per l'Intelligenza Artificiale (http://www.aixia.it) Associazione Italiana per le Science Cognitive Center for Cognitive Science -University and  ...  We thank Burak Topal for his contribution for robotic arm movement and also thank Mehmet Biberci for his effort on vision algorithms. Acknowledgments.  ... 
dblp:conf/aic/Sloman14 fatcat:4dlcqkjksvgahp6tkqvqpm6q4q

Modeling Heart and Brain signals in the context of Wellbeing and Autism Applications: A Deep Learning Approach [article]

Mayor Torres Juan Manuel
2020
Studies with neonates confirms not only differences in amplitude but also in terms of latencies to process faces, thus relating this effect with cortical brain structures maturity, and other subcortical  ...  MEG studies (Leung et al., 2018) also support the over-activation of temporal and insular cortical and subcortical structures such as STS, and Fusiform Gyrus in Autism individuals in comparison with  ...  To isolate the level of signal propagated from the Deep ConvNet we modify the direction of the signal s analyzing the attribution/relevance-based from LRP using Taylor constraint root value denoted by  ... 
doi:10.15168/11572_247209 fatcat:raidwnxldbfjzn2qkq3peg3aq4

VISUAL ANALYTICS AND INTERACTIVE MACHINE LEARNING FOR HUMAN BRAIN DATA

Huang Li
2019
This research focuses on using visualization techniques to help neuroscientists in understanding, developing and applying better machine learning models with human brain data.We built a software platform  ...  for multi-modal visualization and then apply advance techniques to interactively train a machine learning model with fewer data.  ...  Our computational task is to predict the MMSE score using the MRI, demographical and behavior measures.  ... 
doi:10.25394/pgs.8984276.v1 fatcat:w7yudgaqvnbjpl5lm4xw5njlta