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ATZelektronik 2017年第1期12卷 48-53页

作者： Kevin Rassner Machine Learning & Artificial Intelligence ITK Engineering GmbH Rülzheim Deutschland

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study

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JMIR Formative Research 2025年 9卷 e67792页

作者： Berge, Martin A. Paraschiv-Ionescu, Anisoara Kirk, Cameron Küderle, Arne Micó-Amigo, Encarna Becker, Clemens Cereatti, Andrea Del Din, Silvia Engdal, Monika Garcia-Aymerich, Judith Grønvik, Karoline B. Hansen, Clint Hausdorff, Jeffrey M. Helbostad, Jorunn L. Jansen, Carl-Philipp Johnsen, Lars Gunnar Klenk, Jochen Koch, Sarah Maetzler, Walter Megaritis, Dimitrios Müller, Arne Rochester, Lynn Schwickert, Lars Taraldsen, Kristin Vereijken, Beatrix Department of Neuromedicine and Movement Science Norwegian University of Science and Technology Trondheim Norway Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Geriatric Center Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Department of Geriatrics and Rehabilitation Robert Bosch Hospital Stuttgart Germany Department of Electronics and Telecommunications Politecnico di Torino Turin Italy National Institute for Health and Care Research Newcastle Biomedical Research Centre Newcastle University The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom Barcelona Institute for Global Health Barcelona Spain Department of Medicine and Life Sciences Universitat Pompeu Fabra Catalonia Barcelona Spain CIBER Epidemiología y Salud Pública Madrid Spain Department of Neurology University Hospital Schleswig-Holstein Kiel University Kiel Germany Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Medical Center Tel Aviv Israel Department of Physical Therapy Faculty of Medical & Health Sciences Tel Aviv University Tel Aviv Israel Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel Rush Alzheimer’s Disease Center Rush University Medical Center Chicago IL United States Department of Orthopedic Surgery Rush Medical College Rush University Chicago IL United States Department of Orthopaedic Surgery St. Olav’s Hospital Trondheim Norway Institute of Epidemiology and Medical Biometry Ulm University Ulm Germany IB University of Health and Social Sciences Study Centre Stuttgart Stuttgart Germany Department of Sport

Background: Algorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip fracture, who often walk slowly for short durations, is underexplored. Objective: This study examined DMO accuracy and reliability in a hip fracture cohort considering walking bout (WB) duration, physical function, days since surgery, and walking aid use. Methods: In total, 19 community-dwelling participants were real-world monitored for 2.5 hours using a lower back wearable device and a reference system combining inertial modules, distance sensors, and pressure insoles. A total of 6 DMO estimates from 164 WBs from 58% (11/19) of the participants (aged 71-90 years;assessed 32-390 days after surgery;Short Physical Performance Battery [SPPB] scores of 3-12;gait speed range 0.39-1.34 m/s) were assessed against the reference system at the WB and participant level. We stratified by WB duration (all WBs, WBs of >10 seconds, WBs of 10-30 seconds, and WBs of >30 seconds) and lower versus higher SPPB scores and observed whether days since surgery and walking aid use affected DMO accuracy and reliability. Results: Across WBs, walking speed and distance ranged from 0.25 to 1.29 m/s and from 1.7 to 436.5 m, respectively. Estimation of walking speed, cadence, stride duration, number of steps, and distance stratified by WB duration showed intraclass correlation coefficients (ICCs) ranging from 0.50 to 0.99 and mean relative errors (MREs) from –6.9% to 12.8%. Stride length estimation showed poor reliability, with ICCs ranging from 0.30 to 0.49 and MREs from 6.1% to 13.2%. Walking speed and distance ICCs in the higher–SPPB score group ranged from 0.85 to 0.99, and MREs ranged from –10.1% to –1.7%. In the lower–SPPB score group, walking speed and distance ICCs ranged from 0.17 to 0.99, and MREs ranged from 13.5% to 32.6%. There was no discernible effect of time since s

关键词： active aging cadence digital mobility assessment distance free living gait walking speed wearable device

Deep weighted averaging classifiers

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arXiv 2018年

作者： Card, Dallas Zhang, Michael Smith, Noah A. Machine Learning Department Carnegie Mellon University PittsburghPA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Allen Institute for Artificial Intelligence SeattleWA United States

Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text. Despite these gains, however, concerns have been raised about the calibration, robustness, and interpretability of these models. In this paper we propose a simple way to modify any conventional deep architecture to automatically provide more transparent explanations for classification decisions, as well as an intuitive notion of the credibility of each prediction. Specifically, we draw on ideas from nonparametric kernel regression, and propose to predict labels based on a weighted sum of training instances, where the weights are determined by distance in a learned instanceembedding space. Working within the framework of conformal methods, we propose a new measure of nonconformity suggested by our model, and experimentally validate the accompanying theoretical expectations, demonstrating improved transparency, controlled error rates, and robustness to out-of-domain data, without compromising on accuracy or calibration. Copyright © 2018, The Authors. All rights reserved.

关键词： Calibration

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

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arXiv 2024年

作者： LaBella, Dominic Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia Velichko, Yury Wang, Chunhao Kirkpatrick, John Floyd, Scott Reitman, Zachary J. Mullikin, Trey Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Haider, Muhammad Ammar Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Coley, Aaron Huang, Christina Ghanta, Siddharth Schneider, Alex Sharp, Conrad Saluja, Rachit Kofler, Florian Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiation Oncology SUNY Upstate Medical University SyracuseNY United States San FranciscoCA United States Department of Neurosurgery King’s College Hospital London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Guy’s and St Thomas’ NHS Foundation Trust United Kingdom Department of Radiation Oncology University of Washington SeattleWA United States Department of Radiology Northwestern University EvanstonIL United States Department of Radiation Oncology Northwestern University EvanstonIL United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiation Oncology Indiana University IndianapolisIN United States Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States CMH Lahore Medical College Lahore Pakistan Duke University Medical Center School of Medicine DurhamNC United States Sage Bionetworks United States University of Zürich Zürich Switzerland Artificial Intelligence Lab Department of Radiology Mayo Clinic RochesterMN United States MLCommons United States Factored AI United States Center For Federated Learning in Medicine Indiana University IndianapolisIN United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States Intel United States Duke Universi

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. © 2024, CC BY.

关键词： Radiotherapy

Incorporating AEGIS autonomous science into mars science laboratory rover mission operations 15th

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Incorporating AEGIS autonomous science into mars science lab...

15th International Conference on Space Operations, SpaceOps 2018

作者： Francis, Raymond Estlin, Tara Doran, Gary Gaines, Daniel Johnstone, Stephen Montaño, Suzanne Peret, Laurent Mousset, Valérie Gasnault, Olivier Frydenvang, Jens Wiens, Roger C. Schaffer, Steven Pavri, Betina Verma, Vandana Chattopadhyay, Debarati Bornstein, Benjamin Mittal, Nimisha Deflores, Lauren Jet Propulsion Laboratory Machine Learning and Instrument Autonomy Research Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Artificial Intelligence Research Group California Institute of Technology PasadenaCA91109 United States Intelligence and Space Research Division Los Alamos National Laboratory Los AlamosNM87545 United States Telespazio France FIMOC Toulouse France FIMOC Centre National d’Études Spatiales Toulouse France FIMOC Institut de Recherche en Astrophysique et Planétologie Université de Toulouse CNRS CNES UPS France Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Los Alamos National Laboratory Intelligence and Space Research Division Los AlamosNM87545 United States Jet Propulsion Laboratory Artificial Intelligence Research Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Instrument Operations Engineering Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Robot Operations Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Planning and Sequencing Systems Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Electronics and Software Section California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Science Planning Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Instrument System Engineering Group California Institute of Technology PasadenaCA91109 United States

ISBN: (数字)9781624105623

ISBN: (纸本)9781624105623

The AEGIS (Autonomous Exploration for Gathering Increased Science) intelligent targeting software system has been in use on the Mars Science Laboratory (MSL) mission since 2016. The system allows on-board autonomous selection of targets for the ChemCam remote geochemistry instrument based on analysis of images taken by the rover. This paper describes the deployment of AEGIS to MSL and the operational use of the system since rollout to science operations in May of 2016. We describe how MSL Science Operations have adapted to include autonomous target selection, both in procedures and in exploration strategies, and how mission planners and scientists have used AEGIS autonomy to enhance their work on the MSL mission. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Rovers

Constraints on the photon polarisation in b → sγ transitions using Bs0→ ϕe⁺e⁻ decays

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Journal of High Energy Physics 2025年第3期2025卷 1-23页

作者： Aaij, R. Abdelmotteleb, A.S.W. Abellan Beteta, C. Abudinén, F. Ackernley, T. Adefisoye, A.A. Adeva, B. Adinolfi, M. Adlarson, P. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, S. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Aliouche, Z. Alvarez Cartelle, P. Amalric, R. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreianov, A. Andreola, P. Andreotti, M. Andreou, D. Anelli, A. Ao, D. Archilli, F. Argenton, M. Arguedas Cuendis, S. Artamonov, A. Artuso, M. Aslanides, E. Ataíde Da Silva, R. Atzeni, M. Audurier, B. Bacher, D. Bachiller Perea, I. Bachmann, S. Bachmayer, M. Back, J.J. Baladron Rodriguez, P. Balagura, V. Baldini, W. Balzani, L. Bao, H. Baptista de Souza Leite, J. Barbero Pretel, C. Barbetti, M. Barbosa, I.R. Barlow, R.J. Barnyakov, M. Barsuk, S. Barter, W. Bartolini, M. Bartz, J. Basels, J.M. Bashir, S. Bassi, G. Batsukh, B. Battista, P.B. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I.B. Behling, N.A. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Benane, G. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Bernet Andres, S. Bertolin, A. Betancourt, C. Betti, F. Bex, J. Bezshyiko, Ia. Bhom, J. Bieker, M.S. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Blake, T. Blanc, F. Blank, J.E. Blusk, S. Bocharnikov, V. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Bohare, A. Boldyrev, A. Bolognani, C.S. Bolzonella, R. Bondar, N. Bordelius, A. Borgato, F. Borghi, S. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bovill, M. Bowcock, T.J.V. Boyer, A. Bozzi, C. Brea Rodriguez, A. Breer, N. Brodzicka, J. Brossa Gonzalo, A. Brown, J. Brundu, D. Buchanan, E. Buonaura, A. Buonincontri, L. Burke, A.T. Burr, C. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Caillet, A.C. Calabrese, R. Calderon Ramirez, S. Calefice, L. Cali, S. Calvi, M. Calvo Gomez, M. Camargo Magalhaes, P. Cambon Bouzas, J.I. Campana, P. Campora Perez, D.H. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Caravaca-Mora, R. Carbone, A. Carcedo Salgad School of Physics and Astronomy Monash University Melbourne Australia Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Engineering Physics Tsinghua University Beijing China Institute Of High Energy Physics (IHEP) Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Consejo Nacional de Rectores (CONARE) San Jose Costa Rica Université Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Université Paris-Saclay Centre d’Etudes de Saclay (CEA) IRFU Saclay France Gif-Sur-Yvette France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Universität Bonn — Helmholtz-Institut für Strahlen und Kernphysik Bonn Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physikalisches Institut Albert-Ludwigs-Universität Freiburg Freiburg Germany Max-Planck-Institut für Kernphysik (MPIK) Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN

An angular analysis of the Bs0→ ϕe+e− decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of 9 fb−1 at centre... 详细信息

An angular analysis of the Bs0→ ϕe⁺e⁻ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of 9 fb⁻¹ at centre-of-mass energies of 7, 8 and 13 TeV. The analysis is performed in the very low dielectron invariant mass-squared region between 0.0009 and 0.2615 GeV²/c⁴. The longitudinal polarisation fraction of the ϕ meson is measured to be less than 11.5% at 90% confidence level. The ATReCP observable, which is related to the lepton forward-backward asymmetry, is measured to be 0.116 ± 0.155 ± 0.006, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, AT2 and ATImCP, which are sensitive to the virtual photon polarisation, are found to be −0.045 ± 0.235 ± 0.014 and 0.002 ± 0.247 ± 0.016, respectively. The results are consistent with Standard Model predictions. © The Author(s) 2025.

关键词： B Physics Flavour Changing Neutral Currents Hadron-Hadron Scattering Rare Decay

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

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arXiv 2019年

作者： Orlando, Jose Ignacio Fu, Huazhu Breda, Jo-Ao Barbossa Keer, Karelvan Bathula, Deepti R. Diaz-Pinto, Andres Fang, Ruogu Heng, Pheng-Ann Kim, Jeyoung Lee, JoonHo Lee, Joonseok Li, Xiaoxiao Liu, Peng Lu, Shuai Murugesan, Balamurali Naranjo, Valery Phaye, Sai Samarth R. Shankaranarayana, Sharath M. Sikka, Apoorva Son, Jaemin Hengel, Antonvanden Wang, Shujun Wu, Junyan Wu, Zifeng Xu, Guanghui Xu, Yongli Yin, Pengshuai Li, Fei Zhang, Xiulan Xu, Yanwu Bogunovic, Hrvoje Department of Ophthalmology and Optometry Medical University of Vienna Spitalgasse 23 Vienna1090 Australia Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Surgery and Physiology Department Ophthalmology Unit Faculty of Medicine University of Porto Porto Research Group Ophthalmology KU Leuven Leuven Belgium Ropar Rupnagar140001 India Instituto de Investigacione Innovacion en Bioingeniera Universitat Politecnica de Valencia Valencia46022 Spain J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida 32611 United States Department of Computer Science and Engineering Chinese University of Hong Kong 999077 Hong Kong Gachon University Gyeonggi-Do461-701 Korea Republic of Samsung SDS AI Research Center Seoul06765 Korea Republic of Yale University New HavenCT06510 United States Faculty of Science Beijing University of Chemical Technology Beijing100029 China Healthcare Technology Innovation Centre IIT Madras India Department of Electrical Engineering IIT Madras India VUNO Inc Seoul137-810 Korea Republic of Australian Institute for Machine Learning Australia Cleerly Inc New YorkNY10022 United States South China University of Technology Guangzhou510006 China Zhongshan Ophthalmic Center Sun Yat-sen University Guangzhou Shi China Articial Intelligence Innovation Business Baidu Inc China and Cixi Institute of Bio Medical Engineering Chinese Academy of Sciences China

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes dificult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (http://***), held in conjunction with MICCAI 2018. The challenge consistedof two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results. Copyright © 2019, arXiv, All rights reserved.

关键词： Ophthalmology

Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking