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Deep knn for medical image classification 23rd

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Deep knn for medical image classification

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhuang, Jiaxin Cai, Jiabin Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of available data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers. © Springer Nature Switzerland AG 2020.

关键词： Nearest neighbor search

Wideband polarization reconfigurable antenna based on double C-shaped radiators

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Wideband polarization reconfigurable antenna based on double...

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Weilong Liang Long Li Zhao Wu Yanhu Huang Qingxiu Ma Jiajun Liang Nanjing Research Institute of Electronics Technology Nanjing P. R. China School of Physics and Telecommunication Engineering Yulin Normal University Yulin P. R. China Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data Processing Yulin Normal University Yulin P. R. China

An enhancing bandwidth tri-polarization reconfigurable antenna based on double C-shaped radiators is proposed. The antenna consists of a modified ground plane, double C-shaped radiators, and a feedline with a simple parasitic stub. The modified ground plane and the parasitic stub can improve the impedance matching, meanwhile, adjust the axial ratio performance. Two PIN diodes are embedded between the C-shaped radiator and the feedline. Three polarization reconfigurable modes can be achieved caused by switching two PIN diodes state, linear polarization (LP), left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP). As the simulated results show, the −10-dB impedance bandwidth reaches 126.6% (1.76-7.83 GHz) in LP mode and 78.4% (2.96-6.78 GHz) in CP modes. And the proposed antenna has a 3-dB axial ratio (AR) bandwidth of 84% (2.6-6.4 GHz) in two CP modes. Therefore, the proposed antenna can meet the requirement of the modern wireless and satellite communication system.

关键词： Wireless communication Polarization Satellite antennas PIN photodiodes Switches Antenna feeds Broadband antennas

Better Pseudo-label: Joint Domain-aware label and Dual-classifier for Semi-supervised Domain Generalization

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arXiv 2021年

作者： Wang, Ruiqi Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semisupervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. When accurate pseudo-labels are generated for unlabeled source domains, the domain mixup operation is applied to augment new domains between labeled and unlabeled domains, which is beneficial for boosting the generalization capability of the model. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method. Copyright © 2021, The Authors. All rights reserved.

关键词： Image recognition

Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

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arXiv 2022年

作者： Guo, Yong Chen, Yaofo Zheng, Yin Chen, Qi Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui The School of Software Engineering South China University of Technology China Max Planck Institute for Informatics Germany The Weixin Group Tencent China The School of Computer Science The University of Adelaide Australia Tencent AI Lab Tencent China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China

Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world applications. To achieve this goal, existing methods often perform an independent architecture search process for each target budget, which is very inefficient yet unnecessary. More critically, these independent search processes cannot share their learned knowledge (i.e., the distribution of good architectures) with each other and thus often result in limited search results. To address these issues, we propose a Pareto-aware Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the Pareto optimal architecture for any given budget via inference. To train our PNAG, we learn the whole Pareto frontier by jointly finding multiple Pareto optimal architectures under diverse budgets. Such a joint search algorithm not only greatly reduces the overall search cost but also improves the search results. Extensive experiments on three hardware platforms (i.e., mobile device, CPU, and GPU) show the superiority of our method over existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Pareto principle

Argumentation Based Ontology Alignment Extraction 6th

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Argumentation Based Ontology Alignment Extraction

6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Wu, Xiaojing Xue, Xingsi Hu, Wenyu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33 Xuefu South Road Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China School of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

RCAnalyzer:visual analytics of rare categories in dynamic networks

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Frontiers of Information Technology & Electronic engineering 2020年第4期21卷 491-506页

作者： Jia-cheng PAN Dong-ming HAN Fang-zhou GUO Da-wei ZHOU Nan CAO Jing-rui HE Ming-liang XU Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Zhejiang Lab Hangzhou 311100China Department of Computer Science and Engineering Arizona State UniversityTempe 85281USA Intelligent Big Data Visualisation Lab Tongji UniversityShanghai 200082China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China Henan Institute of Advanced Technology Zhengzhou UniversityZhengzhou 450001China

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over *** visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network *** work focuses on detecting anomalous changing patterns in the dynamic network,the rare occurrence of which could damage the development of the entire *** this study,we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic *** proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the *** particular,a novel visualization is introduced,which represents the snapshots of a dynamic network in a series of connected triangular *** clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding *** evaluate our technique via a case study and a user *** evaluation results verify the effectiveness of our system.

关键词： Rare category detection Dynamic network Visual analytics

data Governance: Status, Technologies, Applications and Development Trends

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Data Governance: Status, Technologies, Applications and Deve...

IEEE International Conference on data Science in Cyberspace (DSC)

作者： Haiyang Zhu Xiaofeng Chen Suyan Li Yejin Wang Xinwei Fang Minfeng Zhu The state Key Lab of CAD&CG Zhejiang University Wuchan Zhongda Digital Technology Co. Ltd. Hangzhou China The state Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou Qulian Technology Co. Ltd. Hangzhou China Zhejiang Institute of Mechanical & Electrical Engineering Hangzhou China Wuchan Zhongda Digital Technology Co. Ltd. Hangzhou China Department of Internet and Artificial Intelligence Sir Run Run Shaw Hospital School of Medicine Zhejiang University Hangzhou China Zhejiang University Hangzhou China

data governance involves legal, policy, technologies, and standards measures to ensure security, accuracy, integrity, consistency, and accessibility of data, with the primary objective of unlocking its latent value. In this context, the study initiates by scrutinizing the present status of data governance, subsequently identifying pivotal challenges through the lens of computer sciences. Moreover, to effectively grapple with the intricacies of high-dimensional data, data incompletion, and data redundancy within the realm of data governance, we present a compendium of data governance technology methodologies, encompassing data dimensionality reduction, data imputation, data deduplication, and data visualization. Further enhancing our discourse, we shed light on the typical scenarios of large-scale supply chain enterprises, elucidating data analysis and data intelligence applications. The convergence of these insights seeks to provide a comprehensive overview both data governance technology research and real-world application.

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Structure Embedded Nucleus Classification for Histopathology Images

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arXiv 2023年

作者： Lou, Wei Wan, Xiang Li, Guanbin Lou, Xiaoying Li, Chenghang Gao, Feng Li, Haofeng Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China Pazhou Lab Guangzhou510330 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Pathology Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Artificial Intelligence Thrust The Hong Kong University of Science and Technology at Guangzhou Guangzhou510030 China Department of Colorectal Surgery Department of General Surgery Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou510655 China Shanghai Artificial Intelligence Laboratory Shanghai China

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

Modeling and scaling spontaneous imbibition with generalized fractional flow theory and non-Boltzmann transformation

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arXiv 2024年

作者： Senevirathna, Shaluka Zemlyanova, Anna Kelly, Shaina A. Hu, Qinhong Zhang, Yong Ghanbarian, Behzad Porous Media Research Lab Department of Geology Kansas State University ManhattanKS66506 United States Department of Mathematics Kansas State University ManhattanKS66506 United States Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States State Key Laboratory of Deep Oil and Gas China University of Petroleum [East China China Department of Geological Sciences University of Alabama TuscaloosaAL35487 United States Department of Earth and Environmental Sciences University of Texas at Arlington ArlingtonTX76019 United States Department of Civil Engineering University of Texas at Arlington ArlingtonTX76019 United States Division of Data Science College of Science University of Texas at Arlington ArlingtonTX76019 United States

Spontaneous imbibition (SI) is a process by which liquid is drawn into partially saturated porous media by capillary forces, relevant for subsurface processes like underground fluid storage and withdrawal. Accurate modeling and scaling of counter-current SI have long been challenging. In this study, we proposed a generalized fractional flow theory (GFFT) using the Hausdorff fractal derivative, combined with non-Boltzmann scaling. The model links imbibition distance to time through the power law exponent α/2, where α is the fractal index (0 © 2024, CC BY.

关键词： Porous materials