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Development and Validation of a Deep Learning–Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images
2020
American Journal of Neuroradiology
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Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. ...
Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images. ...
To our knowledge, limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict AD. 14 Currently available ...
doi:10.3174/ajnr.a6848
pmid:33154073
pmcid:PMC7963227
fatcat:j2hxqlk2i5al3oclgsy7knfte4
Artificial Neural Network Based Automated Escalating Tools for Crises Navigation
2018
International Journal of Trend in Scientific Research and Development
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The object detection based on deep learning is an important application in deep learning technology, which is characterized by its strong capability of features learning and feature representation compared ...
In the end, we use trained data set to control the speed and navigate the vehicle in crises situations. ...
The object detection based on deep learning is an important application in deep learning technology, which is characterized by its strong capability of features learning and feature representation compared ...
doi:10.31142/ijtsrd10900
fatcat:jdipgkmp5fgnxbxy5kk3gfohfe
Deep learning for automatic target volume segmentation in radiation therapy: a review
2021
Quantitative Imaging in Medicine and Surgery
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The deep learning-based automatic segmentation method has recently been expanded into target volume automatic segmentation. ...
In this paper, the authors summarized the major deep learning architectures of supervised learning fashion related to target volume segmentation, reviewed the mechanism of each infrastructure, surveyed ...
Footnote Provenance and Peer Review: With the arrangement by the Guest Editors and the editorial office, this article has been reviewed by external peers. ...
doi:10.21037/qims-21-168
pmid:34888194
pmcid:PMC8611469
fatcat:3do6grrz7rcnxoij2ykjllhxue
Deep Learning Based Airway Segmentation Using Key Point Prediction
2021
Applied Sciences
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The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. ...
A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. ...
In this study, a regression neural network-based deep-learning model is proposed, which will enable fully automatic segmentation of airways using CBCT. ...
doi:10.3390/app11083501
doaj:aa997661a0e54959b80a76f7aa050399
fatcat:ox7vzx3qvrculmhncbm4yqjgoq
Author Index
2021
2021 Zooming Innovation in Consumer Technologies Conference (ZINC)
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Using Computer Vision And Deep Learning
Tan, Ziya
Proximal Policy Based Deep Reinforcement Learning Approach for Swarm
Robots
Teslic, Nikola
One Solution for Deterministic Scheduling on GPU for ...
of maximal speed limit traffic signs for use in advanced ADAS
algorithms
Strumberger, Ivana
Current Best Opposition-Based Learning Salp Swarm Algorithm for Global
Numerical Optimization
Optimizing ...
doi:10.1109/zinc52049.2021.9499271
fatcat:zlhtxwxqf5hq7pulgpf4t7sqzu
Front Matter: Volume 11318
2020
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
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using a Base 36 numbering system employing both numerals and letters. ...
SPIE uses a seven-digit CID article numbering system structured as follows: § The first five digits correspond to the SPIE volume number. § The last two digits indicate publication order within the volume ...
using a Base 36 numbering system employing both numerals and letters. ...
doi:10.1117/12.2570206
fatcat:fman4hvttfhhjldpoo2pltdpcm
Deep generative models for automated muscle segmentation in computed tomography scanning
2021
PLoS ONE
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The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. ...
Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient ( ...
A deep learning-based method for automatic segmentation of GMd regions from computed tomography (CT) images of hip OA patients is proposed. 2. ...
doi:10.1371/journal.pone.0257371
pmid:34506602
pmcid:PMC8432798
fatcat:ieqru7kwnrdifds4xz6g6z52xu
Emotional deep learning programming controller for automatic voltage control of power systems
2021
IEEE Access
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This paper designs an emotional deep learning programming controller (EDLPC) for automatic voltage control of power systems. ...
INDEX TERMS Automatic voltage regulator, emotional deep learning programming controller, emotional deep neural network. ...
AUTOMATIC VOLTAGE CONTROLLER BASED ON EMOTIONAL DEEP LEARNING PROGRAMMING CONTROLLER The previous section mainly introduced the algorithm and improvement of the controller. ...
doi:10.1109/access.2021.3060620
fatcat:qf5xqxzxrredbfobszrreu4brq
Biomarkers for Hypoxia, HPVness, and Proliferation from Imaging Perspective
[chapter]
2021
Critical Issues in Head and Neck Oncology
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automatic segmentation of head and neck gross tumor volumes. ...
AbstractRecent advances in quantitative imaging with handcrafted radiomics and unsupervised deep learning have resulted in a plethora of validated imaging biomarkers in the field of head and neck oncology ...
Region of interest (ROI) segmentation is required for radiomics analysis and can be done manually or with automatic segmentation (deep learning). ...
doi:10.1007/978-3-030-63234-2_2
fatcat:w3flgmddefhadnbinuzii2hldm
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
[article]
2021
arXiv
pre-print
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To mitigate this problem, we propose to train a deep neural network to minimize a percentile of the distribution of per-volume loss over the dataset. ...
In this paper, we show that the state-of-the-art deep learning pipeline nnU-Net has difficulties to generalize to unseen abnormal cases. ...
We evaluate the proposed methodology for the automatic segmentation of white matter, ventricles, and cerebellum based on fetal brain 3D T2w MRI. ...
arXiv:2108.04175v1
fatcat:d3zhadjudbhwtfsd4nbcz2ylty
Vision-based Approaches for Automatic Food Recognition and Dietary Assessment: A Survey
2019
IEEE Access
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daily food intake and control eating habits. ...
Recent advancements in machine learning applications and technologies have made it possible to develop automatic or semi-automatic dietary assessment solutions, which is a more convenient approach to monitor ...
DEEP LEARNING APPROACHES Deep learning, a subset of machine learning, is a new approach to learn and train a more effective neural network. ...
doi:10.1109/access.2019.2904519
fatcat:y2oxmcdi2ze37nkux3gurfooka
Object Detection and Motion Tracking
2020
Zenodo
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Tracking, learning and detection in the real video is very important for video surveillance. ...
In this paper we proposed the object detection method.In this project we have implemented deep learning algorithm which uses OpenCV framework. ...
In this Recent Trends in Information Technology and its Application Volume 3 Issue 1 project deep learning algorithm and coffe model framework is used. ...
doi:10.5281/zenodo.3695108
fatcat:7cx2kjma2fgktazocxrfwxfrci
Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation
2019
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
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2018 pre-print arXiv:1810.09582v1 fatcat:ambcutzvcna63ikh3mpguxl3di
We propose a novel deep learning-based approach that directly learns input parameters of motors that move the scan area for motion compensation from optical coherence tomography volumes. ...
In this way, we learn the calibration between object movement and system parameters for motion compensation with arbitrary objects. ...
DISCUSSION AND CONCLUSION We propose a new deep learning-based method for OCT-based motion compensation. ...
doi:10.1117/12.2512823
dblp:conf/miigp/GessertGSS19
fatcat:uemgelpiyrfhdazvo45io6d35y
Real-time inspection system for ballast railway fasteners based on point cloud deep learning
2019
IEEE Access
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In this paper, a real-time inspection system for ballast railway fasteners based on point cloud deep learning is developed. ...
Several deep learning point cloud segmentation models are tested in this dataset and PointNet++ is selected to be deployed in the real-time deep learning module of the system. ...
[38] proposed a fastener defect detection method based on image processing and deep learning networks. ...
doi:10.1109/access.2019.2961686
fatcat:4qquhtnzm5djlklzx7gmdk7eby
Software Vulnerability Analysis and Discovery using Deep Learning Techniques: A Survey
2020
IEEE Access
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A system named Syntax-based, Semantics-based, and Vector Representations (SySeVR) is proposed in [31] as the first systematic framework to detect vulnerability based on deep learning. ...
. • Game changer 3: "VulDeePecker: A Deep Learning-Based System for Vulnerability Detection" by Li et al. ...
doi:10.1109/access.2020.3034766
fatcat:3fpbunyedza2ree3ozle6o63ce
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