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Data mining-based model and risk prediction of colorectal cancer by using secondary health data:A systematic review

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Chinese Journal of Cancer Research 2020年第2期32卷 242-251页

作者： Hailun Liang Lei Yang Lei Tao Leiyu Shi Wuyang Yang Jiawei Bai Da Zheng Ning Wang Jiafu Ji School of Public Administration and Policy Renmin University of ChinaBeijing 100872China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Beijing Office for Cancer Prevention and ControlPeking University Cancer Hospital&InstituteBeijing 100142China Departm ent of Public Policy City University of Hong KongHong Kong SAR999077China Johns Hopkins Primary Care Policy Center BaltimoreMD 21205USA Ddepartment of Neurosurgery Johns Hopkins MedicineBaltimoreMD 21205USA Department of Biostatistics Johns Hopkins Bloomberg School of Public HealthBaltimoreMD 21205USA Department of Computer Science Johns Hopkins Whiting School of EngineeringBaltimoreMD 21205USA Key Laboratory of Carcinogenesis and Translational Research(Ministiy of Education/Beijing) Center of Gastrointestinal SurgeryPeking University Cancer Hospital&InstituteBeijing 100142China

Objective:Prevention and early detection of colorectal cancer(CRC)can increase the chances of successful treatment and reduce *** data mining technologies have been utilized to strengthen the early detection of CRC in primary *** synthesis on the model’s effectiveness is *** systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of ***:The PRISMA framework guided the conduct of this *** obtained papers via Pub Med,Cochrane Library,EMBASE and Google *** appraisal was performed using Downs and Black’s quality *** evaluate the performance of included models,the values of specificity and sensitivity were comparted,the values of area under the curve(AUC)were plotted,and the median of overall AUC of included studies was ***:A total of 316 studies were reviewed for full *** articles were *** studies implement techniques including artificial neural networks,Bayesian networks and decision *** articles reported the overall model ***,the median AUC is 0.8243[interquartile range(IQR):0.8050-0.8886].In the two articles that reported comparison results with traditional models,the data mining method performed better than the traditional models,with the best AUC improvement of 10.7%.Conclusions:The adoption of data mining technologies for CRC detection is at an early *** numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques’effects.

关键词： Systematic review colorectal cancer disease detection data mining

A review of smartphones based indoor positioning: challenges and applications

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arXiv 2020年

作者： Nguyen, Khuong An Luo, Zhiyuan Li, Guang Watkins, Chris School of Computing Engineering and Maths University of Brighton BrightonBN2 4AT United Kingdom Computer Science Department Royal Holloway University of London SurreyTW20 0EX United Kingdom Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients. Copyright © 2020, The Authors. All rights reserved.

关键词： Smartphones

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

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Computational intelligence and neuroscience 2023年第1期2023卷 3018320页

作者： Kun Zhang Pengcheng Lin Jing Pan Peixia Xu Xuechen Qiu Danny Crookes Liang Hua Lin Wang School of Electrical Engineering Nantong University Nantong Jiangsu 226001 China. Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Jiangsu 226001 China. Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation Nantong Jiangsu 226001 China. Department of Radiology Affiliated Hospital 2 of Nantong University Nantong Jiangsu 226001 China. College of Mechanical Engineering Donghua University Shanghai 201620 China. School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast BT7 1NN UK.

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

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Fast ORB-SLAM without Keypoint Descriptors

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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

Multi-agent deployment under the leader displacement measurement: A PDE-based approach

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arXiv 2019年

作者： Wei, Jieqiang Fridman, Emilia Selivanov, Anton Johansson, Karl H. Department of Automatic Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden School of Electrical Engineering Tel Aviv University Israel

We study the deployment of a first-order multiagent system over a desired smooth curve in 3D space. We assume that the agents have access to the local information of the desired curve and their displacements with respect to their closest neighbors, whereas in addition a leader is able to measure his absolute displacement with respect to the desired curve. In this paper we consider the case that the desired curve is a closed C2 curve and we assume that the leader transmit his measurement to other agents through a communication network. We start the algorithm with displacement-based formation control protocol. Connections from this ODE model to a PDE model (heat equation), which can be seen as a reduced model, are then established. The resulting closed-loop system is modeled as a heat equation with delay (due to the communication). The boundary condition is periodic since the desired curve is closed. By choosing appropriate controller gains (the diffusion coefficient and the gain multiplying the leader state), we can achieve any desired decay rate provided the delay is small enough. The advantage of our approach is in the simplicity of the control law and the conditions. Numerical example illustrates the efficiency of the method. Copyright © 2019, The Authors. All rights reserved.

关键词： Partial differential equations

CenterNet3D: An anchor free object detector for point cloud

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arXiv 2020年

作者： Wang, Guojun Wu, Jian Tian, Bin Teng, Siyu Chen, Long Cao, Dongpu The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Data and Computer Science Sun Yat-Sen University Guangdong Guangzhou510275 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point-the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://***/wangguojun2018/CenterNet3d. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

Smart wearable technologies: state of the art and evolution over time through patent analysis and clustering

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Smart wearable technologies: state of the art and evolution ...

作者： Dehghani, Milad Dangelico, Rosa Maria Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

The successful introduction and application of smart wearable technologies (SWTs) will allow the production of new generations of innovative and high value-added products. To this aim, we have built a unique database of 1313 patents included in the Thomson Innovation database, which were registered between 2001 and 2015 in the SWTs domain. This study shows the development trends in SWTs both in general and in different products classes, identifies leading countries and companies in different technological classes, and recognises basic patents within each year. Further, cluster analysis is used to identify the most relevant technological clusters and their evolution over time. This study offers a complete overview of the state of the art and of the evolution of SWTs over time, so providing important insights to researchers and managers who, based on this study results, will be able to make more informed decisions on research directions and technology strategies. © 2018 Inderscience Enterprises Ltd.

关键词： Wearable technology

Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

Model-based joint bit allocation between geometry and color for video-based 3d point cloud compression

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arXiv 2020年

作者： Liu, Qi Yuan, Hui Hou, Junhui Hamzaoui, Raouf Su, Honglei School of Information Science and Engineering Shandong University Qingdao266237 China Shenzhen Research Institute of Shandong University Shenzhen518057 China School of Control Science and Engineering Shandong University Ji’nan250061 China Department of Computer Science City University of Hong Kong Hong Kong School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Qingdao University Qingdao266071 China

Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint bit allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the bit allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal distortion