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ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network [article]

Sahar Yousefi, Hessam Sokooti, Wouter M. Teeuwisse, Dennis F.R. Heijtel, Aart J. Nederveen, Marius Staring, Matthias J.P. van Osch
2021 arXiv   pre-print
In this paper we propose a convolutional neural network (CNN) based model for translating ASL to PET images, which could benefit patients as well as the healthcare system in terms of expenses and adverse  ...  Moreover, we present a new residual-based-attention guided mechanism to improve the contextual features during the training process.  ...  ACKNOWLEDGEMENTS We are very grateful to the Amsterdam University Medical Center location VUmc for acquiring the PET-data of this study. We especially acknowledge the help of Prof.dr.  ... 
arXiv:2103.05116v1 fatcat:xcuuyi44zvbtxlcsqta6pkrcue

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  Papers were selected and subject to review by the editors and conference program committee. Some conference presentations may not be available for publication.  ...  denoising for low-SNR Arterial Spin Labeling (ASL) MRI 11313 0N Artifact reduction in brain magnetic resonance imaging (MRI) by means of a dense residual network with K-space blending (DRN-KB) SESSION  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI [article]

Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang
2020 arXiv   pre-print
In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from  ...  Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of  ...  A. 3D Explainable Residual Self-Attention Convolutional Neural Network (3D ResAttNet) We proposed a 3D explainable residual attention network (3D ResAttNet), a deep convolutional neural network that adopts  ... 
arXiv:2008.04024v1 fatcat:3g3j3qgkqfdchabdpymv6yale4

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Grouped Single Shot MultiBox Detector Sang-gil Lee; Jae Seok Bae; Hyunjae Kim; Jung Hoon Kim; Sungroh Yoon* T-44 A Diagnostic Report Generator from CT Volumes on Liver Tumor with Semi-supervised Attention  ...  N. de With M-138 Learning to Segment 3D Linear Structures Using Only 2D Annotations Mateusz Kozinski*; Agata Mosinska; Mathieu Salzmann; Pascal Fua M-139 A Multiresolution Convolutional Neural Network  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Neural Network and Vocabulary Forest for Image Retrieval DAY 4 -Jan 15, 2021 Chen, Shannan; Wang, Qian; Sun, Qiule; Liu, Bin; Zhang, Jianxin; Zhang, Qiang 941 Second-Order Attention Guided Convolutional  ...  Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications

Sandra Vieira, Walter H.L. Pinaya, Andrea Mechelli
2017 Neuroscience and Biobehavioral Reviews  
We conclude our review by discussing the main promises and challenges of using DL to elucidate brain-based disorders, as well as possible directions for future research.  ...  Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition.  ...  Convolutional neural networks Convolutional neural networks (CNNs) are a special type of feedforward neural networks that were initially designed to process images, and as such are biologically-inspired  ... 
doi:10.1016/j.neubiorev.2017.01.002 pmid:28087243 fatcat:orw3gi6scbhm3ct3r3y5lgojpa

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, Laith Farhan
2021 Journal of Big Data  
It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with  ...  Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications.  ...  Acknowledgements We would like to thank the professors from the Queensland University of Technology and the University of Information Technology and Communications who gave their feedback on the paper.  ... 
doi:10.1186/s40537-021-00444-8 pmid:33816053 pmcid:PMC8010506 fatcat:x2h5qs5c2jbntipu7oi5hfnb6u

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions [article]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy.  ...  Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning.  ...  [127] used a deep neural network for this purpose by exploiting the U-Net architecture [48] with residual connection.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
INDEX TERMS Deep learning, medical imaging, artificial neural networks, survey, tutorial, data sets. 99540 2169-3536  ...  We organize the reviewed literature according to the underlying pattern recognition tasks and further sub-categorize it following a taxonomy based on human anatomy.  ...  Zhang and Chung [137] used a deep neural network for this purpose by exploiting the U-Net architecture [53] with residual connection.  ... 
doi:10.1109/access.2019.2929365 fatcat:arimcbjaxrd3zcsjyzd7abjgd4

Advances in Multimodal Data Fusion in Neuroimaging: Overview, Challenges, and Novel Orientation

Yu-Dong Zhang, Zhengchao Dong, Shui-Hua Wang, Xiang Yu, Xujing Yao, Qinghua Zhou, Hua Hu, Min Li, Carmen Jiménez-Mesa, Javier Ramirez, Francisco J. Martinez, Juan Manuel Gorriz
2020 Information Fusion  
of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification.  ...  Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities.  ...  Acknowledgments This study was partly supported by Royal Society International Ex-  ... 
doi:10.1016/j.inffus.2020.07.006 pmid:32834795 pmcid:PMC7366126 fatcat:3cmhcplb5bf2fgpx3kukifbj74


(:Unkn) Unknown, University, My, Li Bai
for DL training, was proposed for DL-based ASL denoising and BOLD-to-ASL prediction; 2) a novel deep learning neural network that combines dilated convolution and wide activation residual blocks was proposed  ...  to improve the image quality of ASL CBF while reducing ASL acquisition time; 3) a prior-guided and slice-wise adaptive outlier cleaning algorithm was developed for ASL MRI.  ...  Incorporating wide activation residual blocks [28] with a Dilated Convolution Current network architecture for DL-based ASL MRI denoising methods, such as residual network [22] and Dilated Network [  ... 
doi:10.34944/dspace/4045 fatcat:4dqocnxzindcrgdomdw6cvvide

Poster Session I

2013 Neuropsychopharmacology  
Cognitive deficits (attention, working memory, and cognitive flexibility) are considered a core symptom cluster in schizophrenia (SZ); predictive of functional outcome yet not alleviated by current drug  ...  during the 15 s delay (by 33%; dropping to chance levels).  ...  Slifstein, A. Abi-Dargham, and S. Kapur, Arch Gen Psychiatry 69, 776 (2012). 2  ... 
doi:10.1038/npp.2013.279 fatcat:54ipecxjarcvljrvn5fgtgif5u

State of the Art of Audio- and Video-Based Solutions for AAL

Slavisa ALeksic, Michael Atanasov, Jean Calleja Agius, Kenneth Camilleri, Anto Čartolovni, Pau Climent-Pérez, Sara Colantonio, Stefania Cristina, Vladimir Despotovic, Hazım Kemal Ekenel, Ekrem Erakin, Francisco Florez-Revuelta (+27 others)
2022 Zenodo  
Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one's activities. In addition, a single [...]  ...  Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support.  ...  Convolutional Neural Network (CNN) supervised Convolutional Neural Network (CNN) Monitoring/ action recognition/ HCI Accuracy of the classification between different action classes: 87.44% [IV.231] Video  ... 
doi:10.5281/zenodo.6390708 fatcat:6qfwqd2v2rhe5iuu5zgz77ay4i

Modeling & Analysis

2003 NeuroImage  
A new method is developed using Kalman filters to quantify the interaction between regions of a neural network with functional MRI data.  ...  We have applied the algorithm to several MRI Data Sets. Despite the diversity of the images the neural network shows good robustness.  ...  The coefficients are normalized to extract shape information (i.e., excluding translation, rotation, and scaling).  ... 
doi:10.1016/s1053-8119(05)70006-9 fatcat:zff2suxcofbxvetfrwfwcxi3zm

ECR 2012 Book of Abstracts - A - Postergraduate Educational Programme

2012 Insights into Imaging  
To understand the multimodality approach to MI based on PET-CT-MRI. 2. To learn about the different radiotracers that can be used with multimodality equipment. 3.  ...  In this talk, the principal radiotracers and multimodality (PET-CT and PET-MR) devices will be summarised by showing their possible contribution to the early diagnosis, risk stratification and prognosis  ...  Careful subtraction MR (late arterial phase -baseline) has also been shown to be a robust guide to residual disease.  ... 
doi:10.1007/s13244-012-0153-4 pmid:22696127 pmcid:PMC3481066 fatcat:te6ctbtakzh5njsw43geghw3ta
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