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2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 579-590 Learning No-Reference Quality Assessment of Multiply and Singly Distorted Images With Big Data.  ...  ., +, TIP 2020 3612-3625 Learning No-Reference Quality Assessment of Multiply and Singly Distorted Images With Big Data.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TIP 2021 68-79 No-Reference Quality Assessment for Screen Content Images Using Visual Edge Model and AdaBoosting Neural Network.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Table of contents

2020 IEEE Transactions on Image Processing  
Meng, and Y. Qiao 4057 Tensor Oriented No-Reference Light Field Image Quality Assessment ...... W. Zhou, L. Shi, Z. Chen, and J.  ...  Davies 2666 Learning No-Reference Quality Assessment of Multiply and Singly Distorted Images With Big Data .................. ...........................................................................  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, Future [article]

Teofilo E. Zosa
2021 arXiv   pre-print
the contributions and characteristics of each network topology.  ...  This paper will briefly overview some of the state-of-the-art (SoTA) neural network-based segmentation algorithms with a particular emphasis on the most recent architectures, comparing and contrasting  ...  This is accomplished via "skip connections" which add the activations of a hidden layer in the neural network with the linear outputs of a layer further downstream (see Figure 2 ) [27] .  ... 
arXiv:2103.14969v2 fatcat:ikxjpikwrneb3ijt6acerkdobu

DropRegion Training of Inception Font Network for High-Performance Chinese Font Recognition [article]

Shuangping Huangm Zhuoyao Zhong, Lianwen Jin, Shuye Zhang, Haobin Wang
2017 arXiv   pre-print
convolutional neural network (CNN) structure elements, i.e., a cascaded cross-channel parametric pooling (CCCP) and global average pooling, is designed.  ...  In this paper, a DropRegion method is proposed to generate a large number of stochastic variant font samples whose local regions are selectively disrupted and an inception font network (IFN) with two additional  ...  Pairs of adjacent CCCP layers are stacked together, and an extra traditional convolution layer is added below each pair of CCCP layers, resulting in a micro neural network (referred to as mlpconv in paper  ... 
arXiv:1703.05870v2 fatcat:jlq76dz6yvbspgae3s4zbuivvq

Table of contents

2020 IEEE Transactions on Image Processing  
Lee, and A. C. Bovik 4219 Tensor Oriented No-Reference Light Field Image Quality Assessment ...... W. Zhou, L. Shi, Z. Chen, and J.  ...  Farias, and A. C. Bovik 6054 No-Reference Image Quality Assessment: An Attention Driven Approach ............ D. Chen, Y. Wang, and W.  ...  Lin, and Zhang, Y. Tian, K. Wang, W. Zhang, and F.-  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks

Felix Meister, Tiziano Passerini, Chloé Audigier, Èric Lluch, Viorel Mihalef, Hiroshi Ashikaga, Andreas Maier, Henry Halperin, Tommaso Mansi
2021 Frontiers in Physiology  
This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements.  ...  Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia.  ...  ACKNOWLEDGMENTS The authors would like to thank Oscar Camara for the provision of the challenge data as well as the continuous support regarding the data.  ... 
doi:10.3389/fphys.2021.694869 pmid:34733172 pmcid:PMC8558498 fatcat:637etrighze4flgfgcby6a2ica

A comprehensive review on convolutional neural network in machine fault diagnosis [article]

Jinyang Jiao, Ming Zhao, Jing Lin, Kaixuan Liang
2020 arXiv   pre-print
Convolutional neural network, as a typical representative of intelligent diagnostic models, has been extensively studied and applied in recent five years, and a large amount of literature has been published  ...  Then, the fundamental theory from the basic convolutional neural network to its variants is elaborated.  ...  Xing, Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization, Mech. Syst.  ... 
arXiv:2002.07605v1 fatcat:54w3panr35bb7app4y7dfnjeqa

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
(CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels).  ...  Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential  ...  ACKNOWLEDGMENTS We would like to thank our colleagues: Karl Hahn, Qingjie Meng, James Batten, and Jonathan Passerat-Palmbach who provided the insight and expertise that greatly assisted the work, and also  ... 
doi:10.3389/fcvm.2020.00025 pmid:32195270 pmcid:PMC7066212 fatcat:iw7xpnltn5cgbn5ullq2ldy3nq

Deep learning for cardiac image segmentation: A review [article]

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2019 arXiv   pre-print
(CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels).  ...  Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential  ...  Convolutional Neural Networks (CNNs) In this part, we will introduce convolutional neural network (CNN), which is the most common type of deep neural networks for image analysis.  ... 
arXiv:1911.03723v1 fatcat:cwsq5hiaebgkza5ktmtyw553je

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS April 2020 1572-1583 Multi-Scale and Multi-Scope Convolutional Neural Networks for Destination Prediction of Trajectories.  ...  ., +, TITS June 2020 2471-2484 Multi-Scale and Multi-Scope Convolutional Neural Networks for Destination Prediction of Trajectories.  ...  R Radar clutter Outliers-Robust CFAR Detector of Gaussian Clutter Based on the Truncated-Maximum-Likelihood-Estimator in SAR Imagery. Ai, J., 2039 -2049  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

DeepHealth: Review and challenges of artificial intelligence in health informatics [article]

Gloria Hyunjung Kwak, Pan Hui
2020 arXiv   pre-print
The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare.  ...  Despite its notable advantages, there are some key challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label, bias) and model (reliability, interpretability  ...  Convolutional Neural Networks Convolutional neural network (CNN) is an algorithm inspired by biological processing of the animal visual cortex [23, 64, 65] .  ... 
arXiv:1909.00384v2 fatcat:sy7pm2c2uvdd3pal2russn4xri

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
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.  ...  Following the success of convolutional neural networks, Bruna et al.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Table of contents

2021 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
BASED ON 3D CONVOLUTIONAL NEURAL NETWORK Yingjie Feng, Sumei Li, Yongli Chang, Tianjin University, China lxxiv IVMSP-21.6: NO-REFERENCE STEREOSCOPIC IMAGE QUALITY ASSESSMENT ..........................  ...  NEW METHODS TO EVALUATE ................................... 1905 NO-REFERENCE PICTURE AND VIDEO QUALITY MODELS Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, University of Texas at Austin, United States; Yilin  ... 
doi:10.1109/icassp39728.2021.9414617 fatcat:m5ugnnuk7nacbd6jr6gv2lsfby

Non-linear Independent Dual System (NIDS) for Discretization-independent Surrogate Modeling over Complex Geometries [article]

James Duvall, Karthik Duraisamy, Shaowu Pan
2021 arXiv   pre-print
network and a point-wise spatial network in a linear output layer.  ...  Existing Convolutional Neural Network-based frameworks for surrogate modeling require lossy pixelization and data-preprocessing, which is not suitable for realistic engineering applications.  ...  We note that the maps are bilinear in w µ and h x , not in x and µ µ µ due to the non-linear nature of neural networks.  ... 
arXiv:2109.07018v3 fatcat:l7ooyapqmrdivmrlpu3rahzvym
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