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Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

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Nature Communications 2025年第1期16卷 1-20页

作者： Maximilian Zenk David Zimmerer Fabian Isensee Santhosh Parampottupadam Kaushal Parekh Ralf Floca Philipp Vollmuth Klaus Maier-Hein Wolfgang Wick Ujjwal Baid Sarthak Pati Akis Linardos Spyridon Bakas Bhakti Baheti Siddhesh Thakur Micah Sheller Alejandro Aristizabal Hasan Kassem Peter Mattson Alexandros Karargyris Brandon Edwards Patrick Foley Alexey Gruzdev Jason Martin Prashant Shah Russell T. Shinohara Chiharu Sako Michel Bilello Suyash Mohan Christos Davatzikos Gaurav Shukla Annika Reinke Lena Maier-Hein Verena Chung Jake Albrecht Kaisar Kushibar Karim Lekadir Meirui Jiang Quande Liu Cheng Chen Qi Dou Pheng-Ann Heng Youtan Yin Hongzheng Yang Xiaofan Zhang Shaoting Zhang Muhammad Irfan Khan Mojtaba Jafaritadi Esa Alhoniemi Elina Kontio Mohammad Ayyaz Azeem Suleiman A. Khan Leon Mächler Ivan Ezhov Florian Kofler Suprosanna Shit Timo Loehr Bjoern Menze Benedikt Wiestler Hongwei Li Johannes C. Paetzold Himashi Peiris Kamlesh Pawar Shenjun Zhong Zhaolin Chen Gary Egan Mehrtash Harandi Munawar Hayat Ece Isik Polat Gorkem Polat Altan Kocyigit Alptekin Temizel Anup Tuladhar Raissa Souza Nils D. Forkert Pauline Mouches Matthias Wilms Lakshay Tyagi Vishruth Shambhat Shubham Subhas Danannavar Rohit Kalla Vikas Kumar Anand Ganapathy Krishnamurthi Akansh Maurya Sahil Nalawade Chandan Ganesh Ben Wagner Divya Reddy Yudhajit Das Fang F. Yu Ananth J. Madhuranthakam Joseph Maldjian Marco C. Pinho James Holcomb Baowei Fei Gaurav Singh Jianxun Ren Wei Zhang Ning An Youjia Zhang Ying Zhou Qingyu Hu Vasilis Siomos Giacomo Tarroni Jonathan Passerrat-Palmbach Ambrish Rawat Giulio Zizzo Swanand Ravindra Kadhe Jonathan P. Epperlein Stefano Braghin Yuan Wang Renuga Kanagavelu Qingsong Wei Yechao Yang Yong Liu Krzysztof Kotowski Szymon Adamski Bartosz Machura Wojciech Malara Jakub Nalepa Lukasz Zarudzki Yaying Shi Yonghong Yan Hongjian Gao Salman Avestimehr Agus S. Akbar Ekaterina Kondrateva Hua Yang Zhaopei Li Hung-Yu Wu Johannes Roth Camillo Saueressig Alexandre Milesi Michal Futrega Quoc D. Nguyen Nathan J. Gruenhagen Tsung-Ming Huang German Cancer Research Center (DKFZ) Heidelberg Division of Medical Image Computing Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Division for Computational Radiology & Clinical AI University Hospital Bonn Bonn Germany Faculty of Medicine University of Bonn Bonn Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Center for Federated Learning in Medicine Indiana University Indianapolis IN USA Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indianapolis IN USA Medical Research Group MLCommons San Francisco CA USA Indiana University Melvin and Bren Simon Comprehensive Cancer Center Indianapolis IN USA Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Department of Neurological Surgery Indiana University School of Medicine Indianapolis IN USA Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN USA Intel Corporation Santa Clara CA USA Factored Palo Alto CA USA Center for AI and Data Science for Integrated Diagnostics (AI2D) University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Statistics in Imaging and Visualization Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Department of Radiation Oncology Christiana Care Health System Philadelphia PA USA Department of Radiation Oncology Sidney Kimmel Comprehensive Cancer Center Thomas J

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Tissue tropisms of avian influenza A viruses affect their spillovers from wild bird to domestic swine

Tissue tropisms of avian influenza A viruses affect their sp...

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作者： Xiaojian Zhang Fred L.Cunningham Lei Li Katie Hanson-Dorr Alicia Olivier Liyuan Liu Brandon S.Schmit Minhui Guan Jacqueline M.Nolting Andrew S.Bowman Mia Kim Torchetti Thomas J.DeLiberto Xiu-Feng Wan Missouri University Center for Research on Influenza Systems Biology (CRISB) University of Missouri Bond Life Sciences Center University of Missouri Department of Molecular Microbiology and Immunology School of Medicine University of Missouri Department of Basic Sciences College of Veterinary Medicine Mississippi State University Mississippi Field Station National Wildlife Research Center Wildlife Services Animal and Plant Health Inspection Service U.S. Department of Agriculture Department of Population and Pathobiology College of Veterinary Medicine Mississippi State University National Wildlife Disease Program Wildlife Services Animal and Plant Health Inspection Service U.S. Department of Agriculture Department of Veterinary Preventive Medicine The Ohio State University National Veterinary Services Laboratories Veterinary Services U.S. Department of Agriculture Department of Electrical Engineering & Computer Science College of Engineering University of Missouri Department of Pathobiology College of Veterinary Medicine University of Missouri MU Informatics Institute University of Missouri

Having both avian-like receptors (?±2,3-linked sialic acid, SA2,3Gal) and human-like receptors (?±2,6-linked sialic acid, SA2,6Gal), swine are proposed as??úmixing vessel??ùfor generating influenza pandemic strains. Laboratory experiments suggested all HA subtypes of influenza A virus (IAV) can infect swine. However, only sporadic cases of avian

关键词： Tissue tropisms of avian influenza A viruses affect their spillovers from wild bird to domestic swine

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He

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Author Correction: Effect of de novo transcriptome assembly on transcript quantification

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Scientific reports 2019年第1期9卷 18041页

作者： Ping-Han Hsieh Yen-Jen Oyang Chien-Yu Chen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan. Department of Bio-Industrial Mechatronics Engineering National Taiwan University Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw. Genome and Systems Biology Program National Taiwan University and Academia sinica Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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High-Throughput Screening: One-Step Generation of a Drug-Releasing Hydrogel Microarray-On-A-Chip for Large-Scale Sequential Drug Combination Screening (Adv. Sci. 3/2019)

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Advanced Science 2019年第3期6卷 1970014-1970014页

作者： Seo Woo Song Su Deok Kim Dong Yoon Oh Yongju Lee Amos Chungwon Lee Yunjin Jeong Hyung Jong Bae Daewon Lee Sumin Lee Jiyun Kim Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 08826 South Korea Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Nano Systems Institute Seoul National University Seoul 08826 South Korea Interdisciplinary Program in Bioengineering Seoul National University Seoul 08826 South Korea School of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 South Korea

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Correction: Robustness of cancer microbiome signals over a broad range of methodological variation

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Oncogene 2024年第20期43卷 1579页

作者： Gregory D Sepich-Poore Daniel McDonald Evguenia Kopylova Caitlin Guccione Qiyun Zhu George Austin Carolina Carpenter Serena Fraraccio Stephen Wandro Tomasz Kosciolek Stefan Janssen Jessica L Metcalf Se Jin Song Jad Kanbar Sandrine Miller-Montgomery Robert Heaton Rana Mckay Sandip Pravin Patel Austin D Swafford Tal Korem Rob Knight Department of Bioengineering University of California San Diego La Jolla CA USA. Micronoma San Diego CA USA. Feinberg School of Medicine Northwestern University Chicago IL USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Clarity Genomics Antwerp Belgium. School of Life Sciences Arizona State University Tempe AZ USA. Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Program for Mathematical Genomics Department of Systems Biology Columbia University Irving Medical Center New York NY USA. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Malopolska Centre of Biotechnology Jagiellonian University in Kraków Kraków Poland. Algorithmic Bioinformatics Department of Biology and Chemistry Justus Liebig University Gießen Gießen Germany. Department of Animal Sciences Colorado State University Fort Collins CO USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Moores Cancer Center University of California San Diego Health La Jolla CA USA. Department of Obstetrics and Gynecology Columbia University Irving Medical Center New York NY USA. Department of Bioengineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu.

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Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes

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Nature 2022年第7909期605卷 E3页

作者： Jill E Moore Michael J Purcaro Henry E Pratt Charles B Epstein Noam Shoresh Jessika Adrian Trupti Kawli Carrie A Davis Alexander Dobin Rajinder Kaul Jessica Halow Eric L Van Nostrand Peter Freese David U Gorkin Yin Shen Yupeng He Mark Mackiewicz Florencia Pauli-Behn Brian A Williams Ali Mortazavi Cheryl A Keller Xiao-Ou Zhang Shaimae I Elhajjajy Jack Huey Diane E Dickel Valentina Snetkova Xintao Wei Xiaofeng Wang Juan Carlos Rivera-Mulia Joel Rozowsky Jing Zhang Surya B Chhetri Jialing Zhang Alec Victorsen Kevin P White Axel Visel Gene W Yeo Christopher B Burge Eric Lécuyer David M Gilbert Job Dekker John Rinn Eric M Mendenhall Joseph R Ecker Manolis Kellis Robert J Klein William S Noble Anshul Kundaje Roderic Guigó Peggy J Farnham J Michael Cherry Richard M Myers Bing Ren Brenton R Graveley Mark B Gerstein Len A Pennacchio Michael P Snyder Bradley E Bernstein Barbara Wold Ross C Hardison Thomas R Gingeras John A Stamatoyannopoulos Zhiping Weng University of Massachusetts Medical School Program in Bioinformatics and Integrative Biology Worcester MA USA. The Broad Institute of Harvard and MIT Cambridge MA USA. Department of Genetics School of Medicine Stanford University Palo Alto CA USA. Cold Spring Harbor Laboratory Functional Genomics Cold Spring Harbor NY USA. Altius Institute for Biomedical Sciences Seattle WA USA. Department of Medicine University of Washington School of Medicine Seattle WA USA. Department of Cellular and Molecular Medicine Institute for Genomic Medicine Stem Cell Program Sanford Consortium for Regenerative Medicine University of California San Diego La Jolla CA USA. Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA. Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. Ludwig Institute for Cancer Research University of California San Diego La Jolla CA USA. Institute for Human Genetics Department of Neurology University of California San Francisco San Francisco CA USA. Genomics Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. HudsonAlpha Institute for Biotechnology Huntsville AL USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA. Department of Developmental and Cell Biology University of California Irvine Irvine CA USA. Department of Biochemistry and Molecular Biology The Pennsylvania State University University Park PA USA. Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA. Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA. Département de Biochimie et Médecine Moléculaire Université de Montréal Montréal Quebec Canada. Division of Experimental Medicine McGill University Montreal Quebec Canada. Institut de Recherches Cliniques de Montréal (IR

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Modeling helium segregation to the surfaces of plasma-exposed tungsten as a function of temperature and surface orientation

Modeling helium segregation to the surfaces of plasma-expose...

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作者： Blondel, Sophie Hammond, Karl D. Hu, Lin Maroudas, Dimitrios Wirth, Brian D. University of Tennessee Department of Nuclear Engineering KnoxvilleTN37996 United States Oak Ridge National Laboratory Computer Sciences and Mathematics Division Oak RidgeTN37831 United States University of Missouri Department of Chemical Engineering ColumbiaMO65211 United States University of Missouri Nuclear Engineering Program ColumbiaMO65211 United States University of Massachusetts Department of Chemical Engineering AmherstMA01003-9303 United States Oak Ridge National Laboratory Fusion and Materials for Nuclear Systems Division Oak RidgeTN37831 United States

We provide a description of the dependence on surface crystallographic orientation and temperature of the segregation of helium implanted with energies consistent with low-energy plasma exposure to tungsten surfaces. Here, we describe multiscale modeling results based on a hierarchical approach to scale bridging that incorporates atomistic studies based on a reliable interatomic potential to parameterize a spatially dependent drift-diffusion-reaction cluster-dynamics code. An extensive set of molecular dynamics (MD) simulations has been performed at 933 K and/or 1200 K to determine the probabilities of desorption and modified trap mutation that occurs as small, mobile Hen (1 ≥ n ≥ 7) clusters diffuse from the near-surface region toward surfaces of varying crystallographic orientation due to an elastic interaction force that provides the thermodynamic driving force for surface segregation. These near-surface cluster dynamics have significant effects on the surface morphology, the near-surface defect structures, and the amount of helium retained in the material upon plasma exposure, for which we have developed an extensive MD database of cumulative evolution during high-flux helium implantation at 933 K, which we compare to our properly parameterized cluster-dynamics model. This validated model is then used to evaluate the effects of temperature on helium retention and subsurface helium clustering. © 2017 American Nuclear Society.

关键词： Helium

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