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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

C.H. Suh, W.H. Shim, S.J. Kim, J.H. Roh, J.-H. Lee, M.-J. Kim, S. Park, W. Jung, J. Sung, G.-H. Jahng,, for the Alzheimer's Disease Neuroimaging Initiative
2020 American Journal of Neuroradiology  
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

Murugan Venkatesan, S. Gokul, Dr. R. Indra Gandhi
2018 International Journal of Trend in Scientific Research and Development  
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

Hui Lin, Haonan Xiao, Lei Dong, Kevin Boon-Keng Teo, Wei Zou, Jing Cai, Taoran Li
2021 Quantitative Imaging in Medicine and Surgery  
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

Jinyoung Park, JaeJoon Hwang, Jihye Ryu, Inhye Nam, Sol-A Kim, Bong-Hae Cho, Sang-Hun Shin, Jae-Yeol Lee
2021 Applied Sciences  
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)  
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

Thomas M. Deserno, Po-Hao Chen
2020 Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications  
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

Daisuke Nishiyama, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada, Hiroshi Yamada, Tao Song
2021 PLoS ONE  
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

Linfei Yin, Chenwei Zhang, Yaoxiong Wang, Fang Gao, Jun Yu, Lefeng Cheng
2021 IEEE Access  
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]

Sebastian Sanduleanu, Simon Keek, Lars Hoezen, Philippe Lambin
2021 Critical Issues in Head and Neck Oncology  
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]

Lucas Fidon, Michael Aertsen, Nada Mufti, Thomas Deprest, Doaa Emam, Frédéric Guffens, Ernst Schwartz, Michael Ebner, Daniela Prayer, Gregor Kasprian, Anna L. David, Andrew Melbourne (+4 others)
2021 arXiv   pre-print
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

Mohammed A. Subhi, Sawal Md Ali, Mohammed A. Mohammed
2019 IEEE Access  
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

Ashwini Deshmukh, Jahanvi Gupta, Bhagyashree Tulsulkar, Siddhali Sagvekar, Geetanjali Patil
2020 Zenodo  
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

Nils Gessert, Martin Gromniak, Matthias Schlüter, Alexander Schlaefer, Cristian A. Linte, Baowei Fei
2019 Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling  
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

Hao Cui, Jian Li, Qingwu Hu, Qingzhou Mao
2019 IEEE Access  
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

Peng Zeng, Guanjun Lin, Lei Pan, Yonghang Tai, Jun Zhang
2020 IEEE Access  
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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