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Research Square 2021年 rs3rs-571332页

作者： Roth, Holger R. Xu, Ziyue Diez, Carlos Tor Jacob, Ramon Sanchez Zember, Jonathan Molto, Jose Li, Wenqi Xu, Sheng Turkbey, Baris Turkbey, Evrim Yang, Dong Harouni, Ahmed Rieke, Nicola Hu, Shishuai Isensee, Fabian Tang, Claire Yu, Qinji Sölter, Jan Zheng, Tong Liauchuk, Vitali Zhou, Ziqi Moltz, Jan Hendrik Oliveira, Bruno Xia, Yong Maier-Hein, Klaus H. Li, Qikai Husch, Andreas Zhang, Luyang Kovalev, Vassili Kang, Li Hering, Alessa Vilaça, João L. Flores, Mona Xu, Daguang Wood, Bradford Linguraru, Marius George NVIDIA BethesdaMD United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Division of Diagnostic Imaging and Radiology Children’s National Hospital WashingtonDC United States Radiology and Imaging Sciences National Institutes of Health Clinical Center BethesdaMD United States School of Computer Science and Engineering Northwestern Polytechnical University China Heidelberg Germany Lynbrook High School San JoseCA United States Shanghai Jiao Tong University China Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg School of Informatics Nagoya University Japan Biomedical Image Analysis Department United Institute of Informatics Problems Belarus Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany School of Medicine University of Minho Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga/Guimarães Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal Heidelberg Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany 2Ai – Polytechnic Institute of Cávado and Ave Barcelos Portugal School of Medicine and Health Sciences George Washington University WashingtonDC United States

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A;n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020. © 2021, CC BY.

关键词： COVID-19

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CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

arXiv

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

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A data tamper protection method based on block chain 5

A data tamper protection method based on block chain

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5th International Conference on Information Science and Cloud Computing, ISCC 2017

作者： Zhou, Fuken Yu, Zepeng Xie, Yongjie Chen, Tian'en Liao, Yongfa Department of Computer Science Guangdong Neusoft Institute Foshan 528000 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China

data centric storage with network as the carrier is vulnerable to external attacks, resulting in data loss and tampering. We propose a method of tamper proofing for block chaining data without centralization and centralization. According to the technology principle of bitcoin block chain, the network data information is protected by block chain encryption technology, and the security of high information tamper resistant is realized. A block chain data storage algorithm based on hash table is hereby proposed, which can get enough address space to save all the data, and make block chain form a time sequence to achieve the safe transmission of data. The research results show that the data protection method has very high tamper resistance ability because of the high security of encrypted digital block chaining technology. The block chain implements the value transfer without relying on centralized organization. © Copyright owned by the author(s).

关键词： Digital storage

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Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

arXiv

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arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Research on key Technologies of Student Electronic Depository System Based on Blockchain

引用

Journal of Physics: Conference Series 2019年第3期1168卷

作者： Sheping Zhai Jing Li Cheng Qiu Yuanyuan Yang Weixing Zuo School of Computer Science & Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an 710121 China

Aiming at the problem that a large number of students' electronic deposit data is easy to forge and destroy, a student electronic deposit application architecture based on blockchain is proposed. There are some features on blockchain, such as decentralized, the non-tamperable of data, the consensus mechanism and data encryption features, and the electronic depository network. All these features provide a secure and credible storage environment, ensures the traceable of the student's electronic deposit information, the trust of the entire network, and the security of the data. At the same time, an improved PBFT consensus algorithm is proposed, which eliminates the certificate elimination phase to reduce network resource consumption, and constructs a decentralized electronic depository platform in combination with the blockchain to realize the authenticity, integrity and reliability of the electronic certificate data. The further development and application of blockchain technology is prospected, hoping to provide inspiration and reference for future related research.

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Soft-gated warping-GAN for pose-guided person image synthesis

arXiv

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arXiv 2018年

作者： Dong, Haoye Liang, Xiaodan Gong, Ke Lai, Hanjiang Zhu, Jia Yin, Jian School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Intelligent Systems Engineering Sun Yat-sen University School of Computer Science South China Normal University

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints;2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Learning fine-grained expressions to solve math word problems

Learning fine-grained expressions to solve math word problem...

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2017 Conference on Empirical Methods in Natural Language processing, EMNLP 2017

作者： Huang, Danqing Shi, Shuming Yin, Jian Lin, Chin-Yew Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Tencent AI Lab United States Microsoft Research United States

ISBN: (纸本)9781945626838

This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54% relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12% (59% relative) on the TS6 benchmark subset. © 2017 Association for Computational Linguistics.

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Neuroimage: Reports: A new member of the Neuroimage family embracing negative findings, replication studies and registered reports

引用

Neuroimage: Reports 2021年第1期1卷 100001-100001页

作者： Forstmann, Birte U. Chambers, Christopher D. Cohen, Michael X. Hartwigsen, Gesa Kochunov, Peter Van De Ville, Dimitri Yan, Chao-Gan University of Amsterdam Integrative Model-based Cognitive Neuroscience Research Unit Amsterdam Netherlands Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Cardiff United Kingdom Radboud University and Radboud University Medical Center Donders Centre for Medical Neuroscience Netherlands Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Maryland Psychiatric Research Center Department of Psychiatry University of Maryland School of Medicine Baltimore 21228 MD United States Medical Image Processing Laboratory Institute of Bioengineering École Polytechnique Fédérale de Lausanne Geneva Switzerland Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland CAS Key Laboratory of Behavioral Science Institute of Psychology Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China International Big-Data Center for Depression Research Institute of Psychology Chinese Academy of Sciences Beijing China Magnetic Resonance Imaging Research Center Institute of Psychology Chinese Academy of Sciences Beijing China Department of Child and Adolescent Psychiatry Hassenfeld Children's Hospital at NYU Langone New York NY United States

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Research on the Application of Cryptography on the Blockchain

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Journal of Physics: Conference Series 2019年第3期1168卷

作者： Sheping Zhai Yuanyuan Yang Jing Li Cheng Qiu Jiangming Zhao School of Computer Science & Technology Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'anShaanxi 710121 China

Blockchain is an innovative application model that integrates distributed data storage, peer-to-peer transmission, consensus mechanisms, digital encryption technology and other computer technologies. It is decentralized, secure, and Information disclosure. In the blockchain, digital encryption technology has a core position. The security of user information and transaction data is a necessary condition for the promotion of blockchain. The development of cryptography technology promotes and restricts the further development of blockchain. This paper outlines the infrastructure of blockchain, including the data layer, network layer, consensus layer, contract layer and application layer. The principles of encryption technology is introduced briefly, such as hash function, asymmetric cryptosystem, digital signature. The application of cryptography in all levels of blockchain is analyzed, including data layer, network layer, consensus layer, etc. It shows that cryptography runs through the whole blockchain system. The existing security problems of blockchain is analyzed, and the future research direction is expected.

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Design of Convolutional Neural Network Based on FPGA

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Journal of Physics: Conference Series 2019年第6期1168卷

作者： Sheping Zhai Cheng Qiu Yuanyuan Yang Jing Li Yiming Cui School of Computer Science & Technology Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'anShaanxi 710121 China

Recently, FPGAs have been widely used in the implementation of hardware accelerators for CNN, especially on mobile and embedded devices. This paper proposes an FPGA-based CNN accelerator. The highly reusable accelerator function is designed to construct the optimized convolutional neural network and memory optimization with a lower hardware resource consumption. The results show the advantage of performance and power when compared with Core i5 CPU and GTX 960 GPU.

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