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arXiv 2020年

作者： Shi, Jiangbo Gao, Zeyu Zhang, Haichuan Puttapirat, Pargorn Wang, Chunbao Zhang, Xiangrong Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiao tong University Xi'an Shaanxi710049 China Shaan xi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Xi'an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi710061 China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi710071 China

Pathological examination is an important step in cancer diagnosis. Pathologists make diagnosis and pathology report based on observed cell and tissue structure on pathological slides. With the development of statistical machine learning, especially deep learning, automated classifiers are being used to analyze histopathological slides and assist pathologists in diagnosis. Currently, commonly used annotation methods in histopathological slides include image-wise, bounding box, ellipse-wise, pixel-wise. In order to verify the influence of annotation in pathological slide on deep learning model, we design corresponding experiments to test the performance based on annotations with different granularity annotation. In classification, all state-of-the-art deep learning based classifiers perform better when they are trained by the dataset with pixel-wise annotation. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows that finer-grained annotation can not only improve the performance of deep learning models, but help deep learning model extract more accurate phenotypic information from histopathological slides. An accurate acquisitions of phenotypic information can help pathologists to enquire the model based on which regions and features in the slide were mainly used to calculate the prediction, improve the reliability of the model prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

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MedComm 2025年第6期6卷 e70247页

作者： Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China Wenzhou Medical University Wenzhou China UCSC Baskin School of Engineering University of California Santa Cruz California USA Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts USA Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China Stony Brook University Stony Brook New York USA School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging.

关键词： early breast cancer screening digital breast tomosynthesis deep learning public database medical image analysis

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Interactive Ontology Matching Based on Evolutionary Algorithm

Interactive Ontology Matching Based on Evolutionary Algorith...

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International Conference on Computational Intelligence and Security

作者： Xingsi Xue Junfeng Chen Aihong Ren College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of IOT Engineering Hohai University ChanZhou China School of Mathematics and Information Science Baoji University of Arts and Sciences Baoji China

ISBN: (数字)9781728160924

ISBN: (纸本)9781728160931

Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.

关键词： Ontologies Graph theory Pattern matching Evolutionary computation

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Research on SDN Multi Controller Deployment based on K-means++

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Li Yue Chen Junyan Liang Chuxin Lei Xiaochun School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi cloud computing and big data collaborative innovation center Guilin 541001 China Guangxi key laboratory of image and graphic intelligent processing Guilin 541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: the mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated.

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VUSphere: Visual Analysis of Video Utilization in Online Distance Education 13

VUSphere: Visual Analysis of Video Utilization in Online Dis...

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13th IEEE Conference on Visual Analytics Science and Technology, VAST 2018

作者： He, Huan Zheng, Oinghua Dong, Bo Key Laboratory of Intelligent Networks and Network Security Ministry of Education Xi'An Jiaotong University China School of Electronic and Information Engineering Xi'An Jiaotong University China School of Continuing Education Xi'An Jiaotong University China National Engineering Lab for Big Data Analytics Xi'An Jiaotong University China

ISBN: (纸本)9781538668610

Online Distance Education (ODE) provides massive course videos of various specialties for students across the country to learn professional knowledge anytime and anywhere. Analyzing the utilization of these videos from user log data can help academics better understand the learning process of students, evaluate the quality of service provided by regional learning centers, and improve the quality of program curriculum in the future. However, due to the lack of comparable indicators, it is a great challenge to discover the utilization patterns of massive videos and analyze the learning process of large-scale student population from learning log data. In this paper, we introduce a visual analytics system, called VUSphere, to explore the video utilization from multiple perspectives with two proposed indicators. This system offers three coordinated views: A spherical layout overview to depict the overall utilization distribution of videos, courses, and students;a detailed statistics view with four panels to present video utilization statistics of each element from multiple perspectives;and a comparison view to examine the differences in individual elements. Based on the real dataset from our ODE school, several patterns related to video utilization and enrollment are found in the case study with our domain experts. © 2018 IEEE.

关键词： Students

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Efficient trajectory data privacy protection scheme based on laplace's differential privacy

Efficient trajectory data privacy protection scheme based on...

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作者： Gu, Ke Yang, Lihao Liu, Yongzhi Yin, Bo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Central South University Changsha410083 China

Now many applications of trajectory (location) data have facilitated people's daily life. However, publishing trajectory data may divulge individual sensitive information so as to influence people's normal life. On the other hand, if we cannot mine and share trajectory data information, trajectory data will lose its value to serve our society. Currently, because the records of trajectory data are discrete in database, some existing privacy protection schemes are difficult to protect trajectory data. In this paper, we propose a trajectory data privacy protection scheme based on Laplace's differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user's trajectory data;secondly, the algorithm builds the polygons according to the protected points and the adjacent and high frequent accessed points selected from the accessed point database, then the algorithm calculates the polygon centroids;finally, the noises are added to the polygon centroids by the Laplace's differential privacy method, and the new polygon centroids are used to replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher. © 2018 Slovene Society Informatika. All rights reserved.

关键词： Trajectories

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Weighted Feature Pyramid Networks for Object Detection

Weighted Feature Pyramid Networks for Object Detection

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Xiaohan Li Taotao Lai Shuaiyu Wang Quan Chen Changcai Yang Riqing Chen Jinxun Lin Fu Zheng Digital Fujian Institute of Big Data for Agriculture and Forestry College of Computer and information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fu Jian Shu Bo Xun Information Technology Co. Ltd

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.

关键词： Feature extraction Kernel Object detection Agriculture Forestry Semantics big data

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Ship Detection Using the Surface Scattering Similarity and Scattering Power

Ship Detection Using the Surface Scattering Similarity and S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Zhang Zhen Y ang Bo Mao Jian Y ang Yifang Ban Huilin Xiong Shanghai Key Lab. of Intelligent Sensing and Recognition Shanghai Jiao Tong University Shanghai China Jiangxi Science and Technology Normal University Nanchang China Jiangsu Key Laboratory of Grain Big Data Mining and Application Nanjing University of Finance & Economics Nanjing China Department of Electronic Engineering Tsinghua University Beijing China Division of Geoinformatics KTH Royal Institute of Technology Stockholm Sweden

Sea surface and ship have different backscattering mechanisms, in which surface scattering is predominant for sea surface in the low sea state case. Based on this fact, many ship detectors have been developed by suppressing the surface scattering resulted from sea surface. Actually, small ship may also have strong surface scattering sometimes. In such a case, the methods of avoiding using surface scattering features may easily miss the detection of small ships. To verify this point, in this paper, we first analyze the shortcomings of An's method which is based on surface scattering similarity and the power maximization synthesis detector (PMS), and then improve it for detecting small ships more effectively. In order to demonstrate the performance of the proposed method, AIRSAR L-Band Polarimetric SAR dataset is exploited. Comparing to other methods, the new method shows a better ship detection performance.

关键词： Marine vehicles Scattering Sea surface Covariance matrices Detectors Synthetic aperture radar Backscatter

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Weight Learning in Weighted ELM Classification Model Based on Genetic Algorithms

Weight Learning in Weighted ELM Classification Model Based o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Peng Yao Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9781538652152

In cost sensitive classification problems we often suppose to have a known cost matrix in which each element represents the cost of mistakenly classifying an object from one class into another. Weighted least square, which does not equally consider individual classes and therefore assigns a different weight to each class of samples, is a typical approach to dealing with cost sensitive classification problems. Theoretically and experimentally it is confirmed that reasonable class weights will greatly improve classification ability of a learning model. Unfortunately we only know that these weights depend generally on cost matrix but very few methods can be used to specifically determine these weights according to cost matrix. This paper proposes a weighted least square (WLS) model of random weight network and then successfully uses the model in cost sensitive classification. A genetic algorithm to determine weights of different sample classes based on a cost matrix is given. Model analysis and experimental simulations are conducted. Considering the total misclassification cost as the evaluation index, a comparative study shows that our WLS model is far superior to the existing cost sensitive ELM and cost sensitive naive Bayes models.

关键词： Training Genetic algorithms data models Analytical models Indexes Support vector machines Decision trees

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