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ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network
[article]
2021
arXiv
pre-print
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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
2020
Medical Imaging 2020: Image Processing
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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]
2020
arXiv
pre-print
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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]
2018
Lecture Notes in Computer Science
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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
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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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
2017
Neuroscience and Biobehavioral Reviews
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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
2021
Journal of Big Data
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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]
2019
arXiv
pre-print
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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
2019
IEEE Access
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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
2020
Information Fusion
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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
MACHINE LEARNING-BASED ARTERIAL SPIN LABELING PERFUSION MRI SIGNAL PROCESSING
[article]
2020
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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
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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
2022
Zenodo
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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
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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
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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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