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AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

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arXiv 2023年

作者： Gu, Xianming Wang, Lihui Deng, Zeyu Cao, Ying Huang, Xingyu Zhu, Yue-Min Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy. © 2023, CC BY.

关键词： Deep learning

Touchstone Benchmark: Are We on the Right Way for Evaluating AI algorithms for Medical Segmentation?

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

The Incoming Influenza Season—China,the United Kingdom,and the United States,2021–2022

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China CDC weekly 2021年第49期3卷 1039-1045页

作者： Shasha Han Ting Zhang Yan Lyu Shengjie Lai Peixi Dai Jiandong Zheng Weizhong Yang Xiaohua Zhou Luzhao Feng Beijing International Center for Mathematical Research Peking UniversityBeijingChina Harvard Medical School Harvard UniversityBostonMAUSA School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingChina WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK Division for Infectious Diseases Chinese Center for Disease Control and PreventionBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina.

Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)*** has been scarce information to understand the future dynamics of influenza—and under different hypothesis on relaxation of nonpharmaceutical interventions(NPIs)in particular—after the disruptions to seasonal ***:We collected data from public sources in China,the United Kingdom,and the United States,and forecasted the influenza dynamics in the incoming 2021–2022 season under different *** considered Northern China and Southern China separately,due to the sharp difference in the patterns of seasonal *** the United Kingdom,data were collected for England ***:Compared to the epidemics in 2017–2019,longer and blunter influenza outbreaks could occur should NPIs be fully lifted,with percent positivity varying from 10.5 to 18.6 in the studying *** rebounds would be smaller if the maskwearing intervention continued or the international mobility stayed low,but sharper if the mask-wearing intervention was lifted in the middle of influenza ***,influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza ***:The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019(COVID-19)*** light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society,our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control,especially for seasons after long periods of low-exposure to influenza *** influenza activity declines globally during the coronavirus disease 2019(COVID-19)pandemic(1–4).For instance,in China,influenza activity,as measured by percentage of submitted specimens testing

关键词： prevention globally continued

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT data

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arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Topische Calcineurininhibitoren und das Risiko für Lymphome: eine systematische Übersicht und Metaanalyse

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JDDG - Journal of the German Society of Dermatology 2021年第9期19卷 1265-1270页

作者： Wu, Po-Chien Huang, I-Hsin Liu, Che-Wei Huang, Yu-Chen Department of Medical Education Taipei Veterans General Hospital Taipei Taiwan Evidence-Based Medicine Center Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Chang Gung Memorial Hospital Linkou Taoyuan Taiwan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Department of orthopedics Cathay General Hospital Taipei Taiwan Department of Dermatology Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Research center of big data and meta-analysis Wan Fang Hospital Taipei Medical University Taipei Taiwan

Zusammenfassung Die topischen Calcineurininhibitoren (TCI) Tacrolimus und Pimecrolimus haben breite Anwendung als Kortikosteroid-einsparende Wirkstoffe zur Behandlung vieler Hautkrankheiten gefunden. Allerdings wird das Risiko maligner Erkrankungen bei Therapie mit TCI weiterhin kontrovers diskutiert. Unser Ziel war es, den Zusammenhang zwischen TCI und Lymphomen in einer systematischen Übersicht und Metaanalyse zu untersuchen. Dazu wurden geeignete Studien in Online-Datenbanken vom Tag ihrer Einführung bis zum 30. August 2020 identifiziert. Um das TCI-bedingte Risiko für die Lymphom-Entstehung zu bewerten, wurden Kohortenstudien analysiert, in denen die Inzidenzen von Lymphomen mit und ohne TCI-Behandlung verglichen wurden. Es wurden auch Subgruppen von Hodgkin- und Non-Hodgkin-Lymphomen analysiert. Die zusammengefassten Ergebnisse zeigten, dass topisch angewandtes Tacrolimus (RR 1,68, 95 %-KI 1,39–2,04) oder Pimecrolimus (RR 1,40, 95 %-KI 1,13–1,74) das Risiko für die Lymphom-Entstehung signifikant erhöhten. Bei TCI-Anwendern war zudem die Inzidenz von Lymphomen mit 0,02–0,09 % im Vergleich zur Kontrollgruppe mit 0,02–0,06 % erhöht. Außerdem zeigte die Subgruppenanalyse sowohl für Tacrolimus (RR 1,89; 95 %-KI 1,53–2,32) als auch für Pimecrolimus (RR 1,38; 95 %-KI 1,09–1,74) ein signifikant höheres Risiko für Non-Hodgkin-Lymphome, jedoch nicht für Hodgkin-Lymphome. Zusammenfassend war bei mit TCI behandelten Patienten das Risiko für die Entstehung eines Lymphoms, insbesondere eines Non-Hodgkin-Lymphoms, signifikant erhöht.

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Channel Measurements and Sparsity analysis for Air-to-Ground mmWave Communications

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Channel Measurements and Sparsity Analysis for Air-to-Ground...

IEEE International Conference on Communications (ICC)

作者： Bin Ao Jingya Yang Runyu Han Dan Fei Lihong Wang Ning Wang Zhaohui Song Bo Ai School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Henan High-Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou China State Key Laboratory of Millimeter Waves Southeast University Nanjing China Space Information Research Institute Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

A channel measurement system for accurate modeling of the millimeter-wave (mmWave) band air-to-ground (A2G) wireless channel is presented. The measurement system consists of a ground station on a small cart and an air station installed on a custom-made octocopter unmanned aerial vehicle (UAV). Channel sounders at 26 GHz mmWave band with a bandwidth of 1 GHz are mounted on the air station and the ground station. In contrast to the on-the-shelf drones, the custom-made UAV offers the advantages of greater payload capacity and extended flight endurance. The measurement campaign was conducted in a university campus scenario, providing valuable measurement data for further analyses of the A2G channel. To analyze multipath effects in the A2G channel, simulation result of a ray tracing (RT) model is used to identify the principal propagation effects in the actual measurements. Subsequently, the path-loss exponent and the standard deviation of shadowing are calculated based on the measurement. Furthermore, sparsity of the A2G mmWave channel is evaluated through analyses of the Gini index and the Rician $K$ factor. By comparing with the measurement results in the different propagation scenarios presented in the related work, e.g., industrial Internet-of-Things (IIoT), vehicular urban, and laboratory room, unique characteristics of the A2G mmWave channel are discussed.

关键词： Wireless communication Millimeter wave measurements Rician channels Autonomous aerial vehicles Air to ground communication Millimeter wave communication Standards

Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance analysis and Placement Optimization

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arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

关键词：

Personalized Heart Disease Detection via ECG Digital Twin Generation

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arXiv 2024年

作者： Hu, Yaojun Chen, Jintai Hu, Lianting Li, Dantong Yan, Jiahuan Ying, Haochao Liang, Huiying Wu, Jian College of Computer Science and Technology Zhejiang University China Computer Science Department University of Illinois at Urbana-Champaign United States Medical Big Data Center Guangdong Provincial People’s Hospital Southern Medical University China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China School of Public Health Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://***/huyjj/LAVQ-Editor. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms