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Unleashing the Strengths of Unlabeled data in Pan-cancer Abdominal Organ Quantification: The FLARE22 Challenge

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arXiv 2023年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Ma, Shihao Young, Adamo Zhu, Cheng Meng, Kangkang Yang, Xin Huang, Ziyan Zhang, Fan Liu, Wentao Pan, YuanKe Huang, Shoujin Wang, Jiacheng Sun, Mingze Xu, Weixin Jia, Dengqiang Choi, Jae Won Alves, Natália de Wilde, Bram Koehler, Gregor Wu, Yajun Wiesenfarth, Manuel Zhu, Qiongjie Dong, Guoqiang He, Jian Wang, Bo He, Junjun Yang, Hua Yang, Huihua Huang, Bingding Lyu, Mengye Ma, Yongkang Guo, Heng Zhang, Rongguo Maier-Hein, Klaus The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Shanghai AI Laboratory Shanghai200232 China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing211113 China Ocean University of China Qingdao266100 China The Department of Computer Science University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Tinavi Medical Technologies Co. Ltd. Beijing100192 China University of Science and Technology Beijing Beijing100083 China The School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518055 China The Department of Radiological Algorithm Fosun Aitrox Information Technology Co. Ltd. Shanghai200033 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Technology University Shenzhen518000 China Xiamen University Xiamen361005 China Alibaba Beijing100084 China Infervision Medical Technology Co. Ltd. Beijing100025 China Hong Kong Centre for Cerebro-cardiovascular Health Engineering 000000 Hong Kong The Department of Radiology Armed Forces Yangju Hospital Yangju11429 Korea Republic of The Department of Radiology Radboudumc Nijmegen6525XZ Netherlands The Department of Medical Image Computing German Cancer Research Center Heidelberg69120 Germany ShenZhen Yorktal DMIT LLC Shenzhen518100 China Heidelberg69120 Germany The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing210008 China The College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China The College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China The Manteia Technologies Co. Ltd Xiamen China The Alibaba DAMO Academy Beijing China The Peter Munk Cardiac Centre University Health Network Department of Laboratory M

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. © 2023, CC BY-NC-ND.

关键词： Diseases

A Hybrid Fault Diagnosis Method for Rolling Bearing Based on Attention and CNN

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A Hybrid Fault Diagnosis Method for Rolling Bearing Based on...

Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Songtao Wang Qianlong Liu Jun Li Kun Liang Hui Cai Hangwei Tian Shandong Hi-Speed Construction Management Group Co. Ltd. Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Shandong Hi-Speed Group Linteng Highway Co. Ltd. Jinan China School of Qilu Transportation Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

In the field of mechanical manufacturing, rolling bearings are important core components. To achieve stable rotational operation, improving diagnostic accuracy has become an urgent issue. In this paper, a hybrid fault diagnosis method is proposed, which combines attention and convolutional neural network and introduces Savitzky-Golay filter, called Savitzky-Golay Convolutional Attention network (SGCA-Net). The method adopts a dual-branch structure for rolling bearing fault diagnosis, which can effectively eliminate noise, and combines attention mechanism and convolutional neural network to perform feature learning on data. The experimental results show that the proposed method can accurately identify various faults, and has high accuracy and robustness.

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PGGAN: Improve Password Cover Rate Using the Controller

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Journal of Physics: Conference Series 2021年第1期1856卷

作者： Xiaozhou Guo Yi Liu Kaijun Tan Min Jin Huaxiang Lu Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China University of Chinese Academy of Sciences Beijing 100089 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing 200031 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing 100083 China

Password generation model based on generative adversarial network usually has the problem of high duplicate rate, which further leads to low cover rate. In this regard, we propose PGGAN model. It sets up an additional controller network which is similar to the discriminator in the aspect of structure and function. The discriminator and the controller respectively learn the measure between the distribution of generated password with the real password distribution and the uniform distribution, and then use two measures to teach generator meanwhile. By changing the activation function and loss function of the controller, different measure functions can be selected. The experimental results show that compared with GAN, our PGGAN performs better both in cover rate and duplicate rate. Moreover, Wasserstein distance usually has a better effect to the other measure in model. Specifically, PGGAN with Wasserstein distance can increase the cover rate by 3.57% and reduce the duplicate rate by 30.85% on rockyou dataset.

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

Coupled-channel approach to Tcc+ including three-body effects

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Physical Review D 2022年第1期105卷 014024-014024页

作者： Meng-Lin Du Vadim Baru Xiang-Kun Dong Arseniy Filin Feng-Kun Guo Christoph Hanhart Alexey Nefediev Juan Nieves Qian Wang Instituto de Física Corpuscular (centro mixto CSIC-UV) Institutos de Investigación de Paterna Apartado 22085 46071 Valencia Spain Institut für Theoretische Physik II Ruhr-Universität Bochum D-44780 Bochum Germany Institute for Theoretical and Experimental Physics NRC “Kurchatov Institute” Moscow 117218 Russia CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Institute for Advanced Simulation Institut für Kernphysik and Jülich Center for Hadron Physics Forschungszentrum Jülich D-52425 Jülich Germany P.N. Lebedev Physical Institute of the Russian Academy of Sciences 119991 Leninskiy Prospect 53 Moscow Russia Moscow Institute of Physics and Technology 141700 Institutsky lane 9 Dolgoprudny Moscow Region Russia Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China

A coupled-channel approach is applied to the charged tetraquark state Tcc+ recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-body D0D0π+ channel. Special attention is paid to the three-body dynamics in the Tcc+ due to the finite life time of the D*. An approach to the Tcc+ is argued to be self-consistent only if both manifestations of the three-body dynamics, the pion exchange between the D and D* mesons and the finite D* width, are taken into account simultaneously to ensure that three-body unitarity is preserved. This is especially important to precisely extract the pole position in the complex energy plane whose imaginary part is very sensitive to the details of the coupled-channel scheme employed. The D0D0 and D0D+ invariant mass distributions, predicted based on this analysis, are in good agreement with the LHCb data. The low-energy expansion of the D*D scattering amplitude is performed and the low-energy constants (the scattering length and effective range) are extracted. The compositeness parameter of the Tcc+ is found to be close to unity, which implies that the Tcc+ is a hadronic molecule generated by the interactions in the D*+D0 and D*0D+ channels. Employing heavy-quark spin symmetry, an isoscalar D*D* molecular partner of the Tcc+ with JP=1+ is predicted under the assumption that the DD*−D*D* coupled-channel effects can be neglected.

关键词： Hadron-hadron interactions Particle interactions

Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of covid-19 in ct images

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arXiv 2020年

作者： He, Kelei Zhao, Wei Xie, Xingzhi Ji, Wen Liu, Mingxia Tang, Zhenyu Shi, Feng Gao, Yang Liu, Jun Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University China Department of Radiology Second Xiangya Hospital Central South University Changsha Hunan China Department of Radiology Quality Control Center Changsha Hunan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Biomedical Research Imaging Center Department of Radiology University of North Carolina Chapel HillNC United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M2UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M2UNet consists of a patch-level encoder, a segmentation subnetwork for lung lobe segmentation, and a classification subnetwork for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images

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arXiv 2023年

作者： Zhang, Deyun Geng, Shijia Zhou, Yang Xu, Weilun Wei, Guodong Wang, Kai Yu, Jie Zhu, Qiang Li, Yongkui Zhao, Yonghong Chen, Xingyue Zhang, Rui Fu, Zhaoji Zhou, Rongbo Yanqi, E. Fan, Sumei Zhao, Qinghao Cheng, Chuandong Peng, Nan Zhang, Liang Zheng, Linlin Chu, Jianjun Xu, Hongbin Tan, Chen Liu, Jian Tao, Huayue Liu, Tong Chen, Kangyin Jiang, Chenyang Liu, Xingpeng Hong, Shenda HeartVoice Medical Technology Hefei China HeartRhythm-HeartVoice Joint Laboratory Beijing China Heart Center Beijing Chao-Yang Hospital Capital Medical University Beijing China HeartRhythm Medical Beijing China Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Department of Physiology School of Basic Medical Sciences Anhui Medical University Hefei China Department of Anatomy and Histoembryology School of Basic Medical Sciences Anhui Medical University Hefei China Department of Cardiology Peking University People's Hospital Beijing China Department of Neurosurgery The First Affiliated Hospital University of Science and Technology of China Hefei China Heart Center Anhui Chest Hospital Anhui Medical University Hefei China Department of Electrocardiography The First Affiliated Hospital Anhui Medical University Hefei China The Second People's Hospital of Hefei City Hefei China Information Center/Medical Big Data Office The First Affiliated Hospital Anhui Medical University Hefei China Department of Cardiology Hebei Yan Da Hospital Langfang China Network Information Center Second Hospital of Tianjin Medical University Tianjin China Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin China Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province Department of Cardiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China National Institute of Health Data Science Peking University Beijing China Institute of Medical Technology Health Science Center Peking University Beijing China

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (cas) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p Copyright © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

Constraining ultralight vector dark matter with the Parkes Pulsar Timing Array second data release

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arXiv 2022年

作者： Wu, Yu-Mei Chen, Zu-Cheng Huang, Qing-Guo Zhu, Xingjiang Bhat, N.D. Ramesh Feng, Yi Hobbs, George Manchester, Richard N. Russell, Christopher J. Shannon, R.M. School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China School of Physical Sciences University of Chinese Academy of Sciences No. 19A Yuquan Road Beijing100049 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Department of Astronomy Beijing Normal University Beijing100875 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Australia Telescope National Facility CSIRO Astronomy and Space Science P.O. Box 76 EppingNSW1710 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology HawthornVIC3122 Australia Australia

Composed of ultralight bosons, fuzzy dark matter provides an intriguing solution to challenges that the standard cold dark matter model encounters on sub-galactic scales. The ultralight dark matter with mass m ∼ 10−23eV will induce a periodic oscillation in gravitational potentials with a frequency in the nanohertz band, leading to observable effects in the arrival times of radio pulses from pulsars. Unlike scalar dark matter, pulsar timing signals induced by the vector dark matter are dependent on the oscillation direction of the vector fields. In this work, we search for ultralight vector dark matter in the mass range of [2 × 10−24, 2 × 10−22 ]eV through its gravitational effect in the Parkes Pulsar Timing Array (PPTA) second data release. Since no statistically significant detection is made, we place 95% upper limits on the local dark matter density as ρVF . 5 GeV/cm3 for m . 10−23 eV. As no preferred direction is found for the vector dark matter, these constraints are comparable to those given by the scalar dark matter search with an earlier 12-year data set of PPTA. Copyright © 2022, The Authors. All rights reserved.

关键词： Vectors

Role of Left-Hand Cut Contributions on Pole Extractions from Lattice data: case Study for Tcc(3875)+

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Physical Review Letters 2023年第13期131卷 131903-131903页

作者： Meng-Lin Du Arseniy Filin Vadim Baru Xiang-Kun Dong Evgeny Epelbaum Feng-Kun Guo Christoph Hanhart Alexey Nefediev Juan Nieves Qian Wang School of Physics University of Electronic Science and Technology of China Chengdu 611731 China Institut für Theoretische Physik II Ruhr-Universität Bochum D-44780 Bochum Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China Institute for Advanced Simulation Institut für Kernphysik and Jülich Center for Hadron Physics Forschungszentrum Jülich D-52425 Jülich Germany Jozef Stefan Institute Jamova 39 1000 Ljubljana Slovenia CeFEMA Center of Physics and Engineering of Advanced Materials Instituto Superior Técnico Avenida Rovisco Pais 1 1049-001 Lisboa Portugal Instituto de Física Corpuscular (centro mixto CSIC-UV) Institutos de Investigación de Paterna Apartado 22085 46071 Valencia Spain Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China

We discuss recent lattice data for the Tcc(3875)+ state to stress, for the first time, a potentially strong impact of left-hand cuts from the one-pion exchange on the pole extraction for near-threshold exotic states. In particular, if the left-hand cut is located close to the two-particle threshold, which happens naturally in the DD* system for the pion mass exceeding its physical value, the effective-range expansion is valid only in a very limited energy range up to the cut and as such is of little use to reliably extract the poles. Then, an accurate extraction of the pole locations requires the one-pion exchange to be implemented explicitly into the scattering amplitudes. Our findings are general and potentially relevant for a wide class of hadronic near-threshold states.

关键词： Hadron-hadron interactions Heavy quark effective theory Lattice QCD Phenomenology Exotic mesons