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arXiv 2019年

作者： Yang, Jiaqi Xian, Ke Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710129 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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CenterNet3D: An anchor free object detector for point cloud

arXiv

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arXiv 2020年

作者： Wang, Guojun Wu, Jian Tian, Bin Teng, Siyu Chen, Long Cao, Dongpu The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Data and Computer Science Sun Yat-Sen University Guangdong Guangzhou510275 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point-the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://***/wangguojun2018/CenterNet3d. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Dynamical multiple polynomial-based neural networks classifier realized with the aid of dropfilter and dual statistical selection

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Engineering Applications of artificial intelligence 2025年 157卷

作者： Zhen Wang Sung-Kwun Oh Zunwei Fu Seok-Beom Roh Witold Pedrycz Research Center for Big Data and Artificial Intelligence Linyi University Linyi 276005 China School of Automation and Electrical Engineering Linyi University Linyi 276005 Shandong China School of Electrical & Electronic Engineering The University of Suwon 17 Wauan-gil Bongdam-eup Hwaseong-si Gyeonggi-do 18323 South Korea Department of Electronic Engineering Seokyeong University Seoul 02713 South Korea Department of Computer Science The University of Suwon 17 Wauan-gil Bongdam-eup Hwaseong-si Gyeonggi-do 18323 South Korea Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer Istanbul Turkiye

Polynomial neural networks (PNN) have emerged as an effective regression modeling methodology in computational intelligence, relying on its interpretable polynomial nodes to fit complex nonlinear data relationships and the adaptive nature of self-organizing networks. To break the bottleneck of PNN structure in the field of multi-classification, this study designs a dynamical multiple polynomial-based neural networks (DMPNN) classifier, focusing on developing a flexible polynomial network classification methodology that enhances predictive capabilities without sacrificing the advantages of PNN structures. Our approach effectively addresses the challenges of multi-class classification with uncertain class boundaries and reduces computational complexity, which is achieved through the synergy of several proposed techniques. Three key issues underpin the proposed DMPNN: (a) The integration of PNN regression models using the one-against-all strategy can provide effective and scalable solutions to multi-class classification problems, especially for uncertain class boundary issues. (b) The dual statistical selection (DSS) approach aims to eliminate redundant inputs during data processing, reduce the computational burden, and increase the variety of neural network nodes in the model neuron selection stage. (c) The synergy of regularization methods including the ℓ2 norm-based method (ℓ2-LSM) and the DropFilter, is exploited to mitigate potential overfitting in coefficient estimation and enhance the generalization capabilities of the proposed classifier. A series of ablation experiments and parameter analysis were conducted to demonstrate the stability and reliability of the proposed model. Then, we applied DMPNN to 17 publicly available datasets and two engineering applications: Recycling of black plastic wastes and phased resolved partial discharge. The performance results show that the DMPNN model outperforms five classical classifiers and four state-of-the-art (SOTA) class

关键词： Partial discharges

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Road Detection for autonomous truck in mine environment

Road Detection for autonomous truck in mine environment

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International Conference on Intelligent Transportation

作者： Jianlin Tang Xiaowei Lu Yunfeng Ai Bin Tian Long Chen Jiangsu XCMG Construction Machinery Research Institute LTD Xuzhou China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781538670255

Road boundary line detection provides information of safe driving range and ensures the safety of the vehicles, which is an important part in the sensing system. Due to the mine roads are complicated and the road boundary lines are blurred, the existing road detection methods are not *** this paper, a real-time road boundary detection algorithm is proposed based on Lidar sensors. For the uneven ground of mine roads, this paper proposes a ground detection algorithm called Double Meshing. The elevated point is divided into beam areas after filtering the detected ground. Finally, the road boundary points are extracted in the beam area and the road boundary line is fitted. The performance of the proposed method is verified by experiments, which highlights its accuracy and real-time.

关键词： Roads Three-dimensional displays Laser radar Sensors data mining Real-time systems Autonomous vehicles

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Analysis of Model Optimization Strategies for a Low-Resolution Camera-Lidar Fusion Based Road Detection Network

Analysis of Model Optimization Strategies for a Low-Resoluti...

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Chinese automation Congress (CAC)

作者： Chao Yang Liwei Zhu Yunfeng Ai Bin Tian Long Chen Bin Zhao Jiangsu XCMG Construction Machinery Research Institute LTD Xuzhou China VIPioneers (HuiTuo) Inc Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

Road detection is one of the primary tasks for autonomous vehicles. However, it can be challenging to detect the road or the drivable region with only color cameras on the unstructured road. In this study, a low-cost and low-resolution Camera-Lidar fusion based deep segmentation network is proposed to detect the front road region. The fusion network can capture both the color features from the image and spatial features from the point cloud, which can be significantly effective in the unstructured road condition. A deep segmentation convolutional neural network is designed to process both the RGB image and the calibrated Lidar point cloud. The Lidar coordinate system was first transformed into the camera coordinate, and the 3D point cloud information is transformed to the 2D color image, which maintained the depth features and textures of the road. A low-cost, low-resolution solution is proposed by rescaling the original high-resolution images into a low-resolution format to increase the real-time inference speed. A cross fusion segmentation network is trained to process the two different inputs simultaneously. To evaluate the efficient model optimization methodologies, several different criteria and learning rate adjustment methods are evaluated. The models are trained and tested with the KITTI public dataset. Results indicate that low-cost the cross-fusion network can provide a reasonable road detection with an exponential learning rate adjustment.

关键词： Roads Cameras Laser radar Three-dimensional displays Sensors Image color analysis Image segmentation

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Improving AI models for rare thyroid cancer subtype by text guided diffusion models

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Nature Communications 2025年第1期16卷 1-16页

作者： Dai, Fang Yao, Siqiong Wang, Min Zhu, Yicheng Qiu, Xiangjun Sun, Peng Qiu, Cheng Yin, Jisheng Shen, Guangtai Sun, Jingjing Wang, Maofeng Wang, Yun Yang, Zheyu Sang, Jianfeng Wang, Xiaolei Sun, Fenyong Cai, Wei Zhang, Xingcai Lu, Hui Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center of Biostatistics and Data Science Technical Center for Digital Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China Department of Clinical Laboratory Medicine Shanghai Tenth People’s Hospital School of Medicine Tongji University Shanghai China Department of Critical Care Medicine Jiuquan Hospital of Shanghai General Hospital Shanghai Jiaotong University School of Medicine Gansu Jiuquan China Department of Ultrasound Pudong New Area People’s Hospital Affiliated to Shanghai University of Medicine and Health Sciences Shanghai China Department of Automation Tsinghua University Beijing China Medical college Nantong University Jiangsu Nantong China Shcool of Artificial Intelligence University of Chinese Academy of sciences Beijing China Xin’an League People’s Hospital Inner Mongolia Xing’an League China Department of Ultrasound Shanghai Fourth People’s Hospital Affiliated to Tongji University Shanghai China Department of Biomedical Sciences Laboratory Affiliated Dongyang Hospital of Wenzhou Medical University Zhejiang Dongyang China Department of Hepatobiliary pancreatic center Xuzhou City Central Hospital Jiangsu Xuzhou China Department of General Surgery Ruijin Hospital Shanghai Jiaotong University School of medicine Shanghai China Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Jiangsu Nanjing China World Tea Organization Cambridge MA United States Institute of Bioinformatics Shanghai Academy of Experimental Medicine Shanghai China

artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommon thyroid cancer types with ultrasound, where scarce data leads to frequent misdiagnosis. Traditional augmentation strategies do not capture the unique disease variations, hindering model training and performance. To overcome this, we propose a text-driven generative method that fuses clinical insights with image generation, producing synthetic samples that realistically reflect rare subtypes. In rigorous evaluations, our approach achieves substantial gains in diagnostic metrics, surpasses existing methods in authenticity and diversity measures, and generalizes effectively to other private and public datasets with various rare cancers. In this work, we demonstrate that text-guided image augmentation substantially enhances model accuracy and robustness for rare tumor detection, offering a promising avenue for more reliable and widespread clinical adoption. © The Author(s) 2025.

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Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence (vol 3, pg 1081, 2021)

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NATURE MACHINE intelligence 2022年第4期4卷 413-413页

作者： Bai, Xiang Wang, Hanchen Ma, Liya Xu, Yongchao Gan, Jiefeng Fan, Ziwei Yang, Fan Ma, Ke Yang, Jiehua Bai, Song Shu, Chang Zou, Xinyu Huang, Renhao Zhang, Changzheng Liu, Xiaowu Tu, Dandan Xu, Chuou Zhang, Wenqing Wang, Xi Chen, Anguo Zeng, Yu Yang, Dehua Wang, Ming-Wei Holalkere, Nagaraj Halin, Neil J. Kamel, Ihab R. Wu, Jia Peng, Xuehua Wang, Xiang Shao, Jianbo Mongkolwat, Pattanasak Zhang, Jianjun Liu, Weiyang Roberts, Michael Teng, Zhongzhao Beer, Lucian Sanchez, Lorena E. Sala, Evis Rubin, Daniel L. Weller, Adrian Lasenby, Joan Zheng, Chuansheng Wang, Jianming Li, Zhen Schonlieb, Carola Xia, Tian Department of Radiology Tongji Hospital and Medical College Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering University of Cambridge Cambridge UK Alan Turing Institute London UK Department of Radiology Union Hospital of Tongji Medical College Huazhong University of Science and Technology Wuhan China HUST-HW Joint Innovation Lab Wuhan China CalmCar Inc Suzhou China Wuhan Blood Centre Wuhan China MSA Capital Beijing China The National Centre for Drug Screening Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China CardioVascular and Interventional Radiology Radiology for Quality and Operations The Cardiovascular Centre at Tufts Medical Centre Radiology Tufts University School of Medicine Medford OR USA Russell H Morgan Department of Radiology & Radiologic Science Johns Hopkins Hospital & Medicine Institute Baltimore MD USA Department of Radiation Oncology School of Medicine Stanford University Palo Alto CA USA Department of Radiology Wuhan Central Hospital Wuhan China Department of Radiology Wuhan Children’s Hospital Wuhan China Faculty of Information and Communication Technology Mahidol University Salaya Thailand Thoracic/Head and Neck Medical Oncology University of Texas MD Anderson Cancer Centre Houston TX USA Translational Molecular Pathology University of Texas MD Anderson Cancer Centre Houston TX USA Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK Oncology R&D at AstraZeneca Cambridge UK Department of Radiology University of Cambridge Cambridge UK Department of Biomedical Data Science Radiology and Medicine Stanford University Palo Alto USA Department of Hepatobiliary Pancreatic Surgery Affiliated Tianyou Hospital Wuhan University of Science and Technology Wuhan China

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Machine Learning Approaches in Polymer science:Progress and Fundamental for a New

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SmartMat 2025年第1期6卷 102-142页

作者： 482.T.-S.Lin C.W.ColeyH.Mochigaseet al.“Bigsmiles:A Structurally-Based Line Notation for Describing Macromolecules"ACs Central Science 5no.9(2019):1523-1531.483.J.Wu and M.Gu“Perfecting Liquid-State Theories With Machine Intelligence"Journal of Physical Chemistry Letters 14no.47(2023):10545-10552.484.M.Rubinstein and R.H.ColbyPolymer Physics(Oxford University Press2003).485.M.E.DeagenB.Dalle-CortN.J.RebelloT.S.LinD.J.Walshand B.D.Olsen“Machine Translation Between BigSMILES Line Notation and Chemical Structure Diagrams"Macromolecules 57no.1(2023):42-53.486.S.M.McDonaldE.K.AugustineQ.LannersC.RudinL.Catherine Brinsonand M.L.Becker“Applied Machine Learning as a Driver for Polymeric Biomaterials Design"Nature Communications 14no.1(2023):4838.487.B.HuA.Linand L.C.Brinson“Tackling Structured Knowledge Extraction From Polymer Nanocomposite Literature as an NER/RE Task With seq2seq"Integrating Materials and Manufacturing Innovation 13no.3(2024):656-668.488.P.V.CoveneyE.R.Doughertyand R.R.Highfield“Big Data Need Big Theory Too"Philosophical Transactions of the Royal Society A:MathematicalPhysical and Engineering Sciences 374no.2080(2016):201601 489.M.Tang R.ZhangS.Liet al.“Towards a Supertough Thermo-plastic Polyisoprene Elastomer Based on a Biomimic Strategy"Angewandte Chemie International Edition557no.48(2018):15836-15840.490.M.Z.Naser“An Engineer's Guide to Explainable Artificial Intel-ligence and Interpretable Machine Learning:Navigating CausalityForced Goodnessand the False Perception of Inference"Automation in Construction 129(2021):103821 .491.T.K.Patra V.MeenakshisundaramJ.H.Hungand D.S.Simmons“Neural-Network-Biased Genetic Algorithms for Materials Design:Evolutionary Algorithms That LearnACS Combinatorial Science 19no.2(2017):96-107.

Machine learning(ML),material genome,and big data approaches are highly overlapped in their strategies,algorithms,and *** can target various definitions,distributions,and correlations of concerned physical parameters in given polymer systems,and have expanding applications as a new paradigm indispensable to conventional *** inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries,thus facilitating mechanism exploration,target prediction,high-throughput screening,optimization,and rational and inverse *** article summarizes representative progress in the recent two decades focusing on the design,preparation,application,and sustainable development of polymer materials based on the exploration of key physical parameters in the composition-process-structure-property-performance *** integration of both data-driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically *** the construction and application of robust ML models,strategies and algorithms to deal with variant tasks in polymer science are still in rapid *** challenges and prospects are then *** believe that the innovation in polymer materials will thrive along the development of ML approaches,from efficient design to sustainable applications.

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Advancing covid-19 diagnosis with privacy-preserving collaboration in artificial intelligence

arXiv

引用

arXiv 2021年

作者： Bai, Xiang Wang, Hanchen Ma, Liya Xu, Yongchao Gan, Jiefeng Fan, Ziwei Yang, Fan Ma, Ke Yang, Jiehua Bai, Song Shu, Chang Zou, Xinyu Huang, Renhao Zhang, Changzheng Liu, Xiaowu Tu, Dandan Xu, Chuou Zhang, Wenqing Wang, Xi Chen, Anguo Zeng, Yu Yang, Dehua Wang, Ming-Wei Holalkere, Nagaraj Halin, Neil J. Kamel, Ihab R. Wu, Jia Peng, Xuehua Wang, Xiang Shao, Jianbo Mongkolwat, Pattanasak Zhang, Jianjun Liu, Weiyang Roberts, Michael Teng, Zhongzhao Beer, Lucian Sanchez, Lorena Escudero Sala, Evis Rubin, Daniel Weller, Adrian Lasenby, Joan Zheng, Chuangsheng Wang, Jianming Li, Zhen Schönlieb, Carola-Bibiane Xia, Tian Department of Radiology Tongji Hospital and Medical College Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering University of Cambridge Cambridge United Kingdom Department of Radiology Union Hospital of Tongji Medical College Huazhong University of Science and Technology Wuhan China HUST-HW Joint Innovation Lab Wuhan China CalmCar Inc Suzhou China Wuhan Blood Centre Wuhan China MSA Capital Beijing China The National Centre for Drug Screening Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China CardioVascular and Interventional Radiology Radiology for Quality and Operations The Cardiovascular Centre at Tufts Medical Centre Radiology Tufts University School of Medicine Medford United States Russell H Morgan Department of Radiology & Radiologic Science Johns Hopkins Hospital & Medicine Institute Baltimore United States Department of Radiation Oncology School of Medicine Stanford University Palo Alto United States Department of Radiology Wuhan Central Hospital Wuhan China Department of Radiology Wuhan Children's Hospital Wuhan China Faculty of Information and Communication Technology Mahidol University Thailand Thoracic/Head and Neck Medical Oncology Translational Molecular Pathology University of Texas MD Anderson Cancer Centre Houston United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Oncology R&D at AstraZeneca Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Department of Biomedical Data Science Radiology and Medicine Stanford University Palo Alto United States Alan Turing Institute London United Kingdom Department of Hepatobiliary Pancreatic Surgery Affiliated Tianyou Hospital Wuhan University of Science and Technology Wuhan China

artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health. © 2021, CC BY-NC-SA.

关键词： Diagnosis

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