检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

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Impact of scanner variability on lymph node segmentation in computational pathology

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Journal of Pathology Informatics 2022年 13卷 100127-100127页

作者： Khan, Amjad Janowczyk, Andrew Müller, Felix Blank, Annika Nguyen, Huu Giao Abbet, Christian Studer, Linda Lugli, Alessandro Dawson, Heather Thiran, Jean-Philippe Zlobec, Inti Institute of Pathology University of Bern Murtenstrasse 31 Bern CH-3008 Switzerland Case Western Reserve University Department of Biomedical Engineering Cleveland 44106 OH United States Department of Oncology Lausanne University Hospital and Lausanne University Lausanne Switzerland Swiss Federal Institute of Technology Lausanne (EPFL) Signal Processing Laboratory (LTS5) Lausanne Switzerland Institute of Complex Systems (iCoSyS) University of Applied Sciences and Arts Western Switzerland Delémont Switzerland Document Image and Video Analysis (DIVA) Research Group Department of Informatics University of Fribourg Fribourg Switzerland Department of Radiology Lausanne University Hospital Lausanne University Centre d'Imagerie Biomédicale (CIBM) Lausanne Switzerland Institute of Pathology City Hospital Triemli Zürich Switzerland

Computer-aided diagnostics in histopathology are based on the digitization of glass slides. However, heterogeneity between the images generated by different slide scanners can unfavorably affect the performance of computational algorithms. Here, we evaluate the impact of scanner variability on lymph node segmentation due to its clinical importance in colorectal cancer diagnosis. 100 slides containing 276 lymph nodes were digitized using 4 different slide scanners, and 50 of the lymph nodes containing metastatic cancer cells. These 400 scans were subsequently annotated by 2 experienced pathologists to precisely label lymph node boundary. Three different segmentation methods were then applied and compared: Hematoxylin-channel-based thresholding (HCT), Hematoxylin-based active contours (HAC), and a convolution neural network (U-Net). Evaluation of U-Net trained from both a single scanner and an ensemble of all scanners was completed. Mosaic images based on representative tiles from a scanner were used as a reference image to normalize the new data from different test scanners to evaluate the performance of a pre-trained model. Fine-tuning was carried out by using weights of a model trained on one scanner to initialize model weights for other scanners. To evaluate the domain generalization, domain adversarial learning and stain mix-up augmentation were also implemented. Results show that fine-tuning and domain adversarial learning decreased the impact of scanner variability and greatly improved segmentation across scanners. Overall, U-Net with stain mix-up (Matthews correlation coefficient (MCC) = 0.87), domain adversarial learning (MCC = 0.86), and HAC (MCC = 0.87) were shown to outperform HCT (MCC = 0.81) for segmentation of lymph nodes when compared against the ground truth. The findings of this study should be considered for future algorithms applied in diagnostic routines. © 2022 The Author(s)

关键词： Colorectal cancer Computational pathology Domain generalization Fine tuning Lymph node Lymph node segmentation Scanner variability Whole slide image

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

引用

Chinese Control Conference (CCC)

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant (LDTI) systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Generators System performance Benchmark testing Sensitivity Degradation Fault detection Aerospace electronics

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X-ray and gamma-ray timing of GRB 180720B, GRB 181222B, GRB 211211A and GRB 220910A observed with Fermi and ASIM

arXiv

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arXiv 2024年

作者： Caballero-García, M.D. Göǧüş, E. Navarro-González, J. Atapin, K.E. Sonbas, E. Uzuner, M. Castro-Tirado, A.J. Pandey, S.B. Gupta, Rahul Ror, A.K. Hu, Y.-D. Wu, S.-Y. Sánchez-Ramirez, R. Guziy, S. Christiansen, F. Connell, P.H. Neubert, T. Østgaard, N. Adsuara, J.E. Gordillo-Vázquez, F.J. Fernández-Garcia, E. Pérez-Garcia, I. Reglero, V. Glorieta de la Astronomía s/n GranadaE-18008 Spain Sabancı University Faculty of Engineering and Natural Sciences İstanbul34956 Turkey Image Processing Laboratory University of Valencia Valencia Paterna Spain Sternberg Astronomical Institute Moscow State University Universitetsky pr. 13 Moscow119991 Russia Department of Physics Adiyaman University Adiyaman02040 Turkey Department of Physics The George Washington University WashingtonDC20052 United States Unidad Asociada al CSIC Departamento de Ingeniería de Sistemas y Automática Escuela de Ingenierías Universidad de Málaga Málaga Spain Manora Peak Nainital263002 India Astrophysics Science Division NASA Goddard Space Flight Center Mail Code 661 GreenbeltMD20771 United States INAF Osservatorio Astronomico di Brera Via E. Bianchi 46 LC MerateI-23807 Italy National Space Institute Technical University of Denmark Kgs. Lyngby Denmark Birkeland Centre for Space Science Department of Physics and Technology University of Bergen Norway

We present a timing study of the gamma and X-ray observations and analysis of a sample of bright gamma-ray bursts (GRBs;i.e. GRB 180720B, GRB 181222B, GRB 211211A and GRB 220910A), including the very bright and long GRB 211211A (a.k.a. kilonova candidate). They have been detected and observed by the Atmosphere-Space Interactions Monitor (ASIM) installed on the International Space Station (ISS) and the Gamma-ray Burst Monitor (GBM) on-board the Fermi mission. The early (T − T0≈ s) and high-energy (0.3-20 MeV) ASIM High Energy Detector (HED) and (150 keV-30 MeV) Fermi (BGO) light curves show well-defined peaks with a low quasi-periodic oscillation (QPO) frequency between 2.5−3.5 Hz that could be identified with the spin of the neutron star in the binary mergers originating these GRBs. These QPOs consist on the first detection of low-frequency QPOs (≤10 Hz) detected in magnetars so far. We also detect a strong QPO at 21.8 − 22 Hz in GRB 181222B together with its (less significant) harmonics. The low-frequency QPO would correspond to the signal of the orbiting neutron star (NS) previous to the final coalescence giving rise to the gravitational-wave (GW) signal. Copyright © 2024, The Authors. All rights reserved.

关键词： Gamma rays

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Interval probabilistic fuzzy WordNet

arXiv

引用

arXiv 2021年

作者： Alizadeh-Q, Yousef Minaei-Bidgoli, Behrouz Hossayni, Sayyed-Ali Akbarzadeh-T, Mohammad-R. Recupero, Diego Reforgiato Rajati, Mohammad-Reza Gangemi, Aldo Data Mining Lab School of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Agents Research Lab TECNIO Centre EASY ViCOROB Research Institute Campus de Montilivi University of Girona Girona Catalonia Spain Italy Laboratoire d'Informatique de Paris Nord Université Paris 13 Sorbonne Paris Cité-CNRS France Signal and Image Processing Institute University of Southern California Los AngelesCA90089-02564 United States

WordNet lexical-database groups English words into sets of synonyms called "synsets." Synsets are utilized for several applications in the field of text-mining. However, they were also open to criticism because although, in reality, not all the members of a synset represent the meaning of that synset with the same degree, in practice, they are considered as members of the synset, identically. Thus, the fuzzy version of synsets, called fuzzy-synsets (or fuzzy word-sense classes) were proposed and studied. In this study, we discuss why (type-1) fuzzy synsets (T1 F-synsets) do not properly model the membership uncertainty, and propose an upgraded version of fuzzy synsets in which membership degrees of word-senses are represented by intervals, similar to what in Interval Type 2 Fuzzy Sets (IT2 FS) and discuss that IT2 FS theoretical framework is insufficient for analysis and design of such synsets, and propose a new concept, called Interval Probabilistic Fuzzy (IPF) sets. Then we present an algorithm for constructing the IPF synsets in any language, given a corpus and a word-sense-disambiguation system. Utilizing our algorithm and the open-American-online-corpus (OANC) and UKB word-sense-disambiguation, we constructed and published the IPF synsets of WordNet for English language. Copyright © 2021, The Authors. All rights reserved.

关键词： Ontology

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3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

3R: Word and Phoneme Edition based Data Augmentation for Lex...

引用

International Conference on Computational Intelligence and Security

作者： Aihua Zheng Naipeng Ye Xiao Wang Xiao Song Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Peking University Shenzhen Institute Shenzhen China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.

关键词： Training Vocabulary Semantics Training data Predictive models Noise measurement Security

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A review on InSAR phase denoising

arXiv

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arXiv 2020年

作者： Xu, Gang Gao, Yandong Li, Jinwei Xing, Mengdao State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou China Xi'an Institute of Space Radio Technology Xi'an710071 China National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing. Copyright © 2020, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Pure Play Ultrasonic 3D Positioning System with Unsynchronized Beacons and Receivers

Pure Play Ultrasonic 3D Positioning System with Unsynchroniz...

引用

IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems, COMCAS

作者： Guy Dascalu Omer Movshovits Alon Eilam Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterbi Faculty of Electrical Engineering Technion – Israel Institute of Technology Technion City Haifa Israel

This paper describes a work on 3D ultrasonic positioning system for mobile devices such as cellular phones and IoT devices with built-in microphones. The uniqueness of the described system is its ability to provide simultaneous and accurate positioning of multiple devices, based on ultrasonic signals transmitted by stationary beacons, while the devices are sampling the signals by their internal, unsynchronized clocks. Auxiliary information required for synchronization is collected and broadcasted by a monitor unit through an ultrasonic modem. The system is considered to be "pure play ultrasonic" since no wiring or radio transmission is used. The number of devices positioned simultaneously is practically unlimited since they do not emit any energy, and their position calculation is performed locally. In the demonstrated system the positioned devices were cellular phones, operating at a sampling rate of 44,100 samples per second. This sampling rate enables using ultrasonic signals beyond the human hearing range.

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3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

arXiv

引用

arXiv 2022年

作者： Chen, Zhifeng Liao, Congyu Cao, Xiaozhi Poser, Benedikt A. Xu, Zhongbiao Lo, Wei-Ching Wen, Manyi Cho, Jaejin Tian, Qiyuan Wang, Yaohui Feng, Yanqiu Xia, Ling Chen, Wufan Liu, Feng Bilgic, Berkin School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Radiology Stanford University Stanford CA United States Maastricht Brain Imaging Center Faculty of Psychology and Neuroscience University of Maastricht Netherlands Department of Radiotherapy Cancer Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Science Guangzhou China Siemens Medical Solutions BostonMA United States Department of Chemical Pathology The Chinese University of Hong Kong Hong Kong Division of Superconducting Magnet Science and Technology Institute of Electrical Engineering Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1×1.1×1.0 mm3 resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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