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Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
[article]
2022
arXiv
pre-print
Preserved Fulltext
The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. ...
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. ...
Acknowledgments This work was under the support of the National Institutes of Health (NIH) through grant R01 CA224140 and a research contract from Siemens Medical Solutions USA, Inc. ...
arXiv:2206.06341v1
fatcat:747ptaqtlreanh2zsket2yhhce
Applications of artificial intelligence in nuclear medicine image generation
2021
Quantitative Imaging in Medicine and Surgery
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This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical ...
It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. ...
(59, 60) demonstrated a GAN network's feasibility in predicting whole-body pCT/corrected PET images. ...
doi:10.21037/qims-20-1078
pmid:34079744
pmcid:PMC8107336
fatcat:36vdnuatljbmzayjw3azmrtdee
Deep Learning in Medical Image Registration: A Review
[article]
2019
arXiv
pre-print
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We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets. ...
A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. ...
Award, a philanthropic award provided by the Winship Cancer Institute of Emory University. ...
arXiv:1912.12318v1
fatcat:kuvckosqd5hp7asg6dofhuiis4
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan ...
, Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention; JBHI Aug. 2020 2230-2237 Hoogi, A., Mishra, A., Gimenez, F., Dong, J., and Rubin ...
., +, JBHI May 2020 1519-1527 Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Vision Transformers in Medical Computer Vision – A Contemplative Retrospection
[article]
2022
arXiv
pre-print
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We hope that this review article will open future directions for researchers in medical computer vision. ...
Along with this, we also demystify several imaging modalities used in Medical Computer Vision. ...
This architecture utilized the local as well as global features and then used them collectively to model the long and short-term dependencies. ...
arXiv:2203.15269v1
fatcat:wecjpoikbvfz5cygytqpktoxdq
2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25
2021
IEEE journal of biomedical and health informatics
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
The Author Index contains the primary entry for each item, listed under the first author's name. ...
Aziz, M.H., +, JBHI May 2021 1385-1396 Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network. ...
doi:10.1109/jbhi.2022.3140980
fatcat:ufig7b54gfftnj3mocspoqbzq4
The promise of artificial intelligence and deep learning in PET and SPECT imaging
2021
Physica medica (Testo stampato)
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A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. ...
Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the ...
Using recurrent neural networks to decrease the scanning time and/ or injected activity, especially in low-count dynamic PET imaging studies would be an interesting field of research. ...
doi:10.1016/j.ejmp.2021.03.008
pmid:33765602
fatcat:onw4fm22y5cxndxiwyy5bdw4t4
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be ...
We also outline the limitations of existing techniques and discuss potential directions for future research. ...
The advantage of a DCRNN is its ability to handle long-term dependencies because of the recurrent network architectures. ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
An Overview on Visual SLAM: From Tradition to Semantic
2022
Remote Sensing
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Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. ...
For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. ...
Long short-term Memory Networks (LSTM) are one of the most common recurrent neural networks [178] . ...
doi:10.3390/rs14133010
fatcat:g45tav2qc5gchp46n6eunjvb2i
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting Ghost Target Detection in 3D Radar Data Using Point Cloud Based Deep Neural Network DAY 4 -Jan 15, 2021 Kocaman, ...
Confocal Microscopy Using U-Net
Regression Map
DAY 3 -Jan 14, 2021
Aprea, Federica; Marrone, Stefano;
Sansone, Carlo
1211
Neural Machine Registration for Motion Correction in Breast DCE-
MRI ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
Real-time People Tracking and Identification from Sparse mm-Wave Radar Point-clouds
2021
IEEE Access
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INDEX TERMS mm-wave radar, person identification, point-clouds, multi-target tracking, convolutional neural networks. ...
Specifically, it achieves accuracies as high as 91.62%, operating at 15 frames per seconds, in identifying three subjects that concurrently and freely move in an unseen indoor environment, among a group ...
In [21] , a recurrent neural network with long short-term memory (LSTM) cells is used for the identification. ...
doi:10.1109/access.2021.3083980
fatcat:ub6ylgaxxbbgzjtdgpiirtwpaa
Leveraging Deep Neural Networks to Improve Numerical and Perceptual Image Quality in, Low-dose Preclinical PET Imaging
2021
Computerized Medical Imaging and Graphics
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Visual inspection and quantitative assessment based on quality metrics proved the superior performance of the DL methods in low-count small animal PET studies, paving the way for a more detailed exploration ...
Albeit being extensively explored in clinical studies, to the best of our knowledge, there is a lack of studies exploring the feasibility of DL-based image denoising in low-count small animal PET imaging ...
This work was extended for whole-body (WB) PET imaging using modified cycle-consistent generative adversarial network (CycleGAN) and residual neural network (ResNet) models (Sanaat et al., 2021) . ...
doi:10.1016/j.compmedimag.2021.102010
pmid:34784505
fatcat:msxl4z7lkrehvjoojrqry5ota4
Program
2020
2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)
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This paper proposes a manufacturing quality prediction method, called MTF-CLSTM, to integrate the Markov transition field (MTF) model and the convolutional long short-term memory (CLSTM) neural network ...
In the proposed system, 3D convolution neural network (3D-CNN) and long short-term memory (LSTM) deep learning algorithms are adopted to generate the action models from depth camera modality and inertial ...
The entire framework consists of one earbud-like BCI device to collect EEG signals, a deep learning decoder to extract the data features using Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN ...
doi:10.1109/icce-taiwan49838.2020.9258230
fatcat:g25vw7mzvradxna2grlzp6kgiq
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
[article]
2021
arXiv
pre-print
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1704.05519v2 fatcat:vg2fezsgyzgptbpbvnwcpw24qyOther Versions
While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. ...
, scene understanding, and end-to-end learning for autonomous driving. ...
[684] use a similar approach, but in addition, they spatially correlate each element of the output of the CNN using Long Short-Term Memory (LTSM) units. ...
arXiv:1704.05519v3
fatcat:xiintiarqjbfldheeg2hsydyra
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation
[article]
2018
arXiv
pre-print
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As Convolutional Neural Networks (CNNs) have lastly demonstrated superior performance for the machine learning task of image semantic segmentation, the pipeline also includes a fully automated CNN algorithm ...
Within this thesis we propose a platform for combining Augmented Reality (AR) hardware with machine learning in a user-oriented pipeline, offering to the medical staff an intuitive 3D visualization of ...
There are different variants of ANNs, such as deep multilayer perceptron (MLP), convolutional neural network (CNN), recursive neural network (RNN), recurrent neural network (RNN), long short-term memory ...
arXiv:1808.04929v1
fatcat:ruvry5jja5bfpbmjbez2dyvx5a
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