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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Classes and phyla of the kingdom Fungi

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Fungal Diversity 2024年第1期124卷 1-165页

作者： Nalin N.Wijayawardene Kevin D.Hyde Kirill V.Mikhailov Gabor Peter Andre Aptroot Carmen L.A.Pires‑Zottarelli Bruno T.Goto Yuri S.Tokarev Danny Haelewaters Samantha C.Karunarathna Paul M.Kirk Andre L.C.M.de A.Santiago Ramesh K.Saxena Nathan Schoutteten Madhara K.Wimalasena Vladimir V.Aleoshin Abdullah M.S.Al‑Hatmi Kahandawa G.S.U.Ariyawansa Amanda R.Assuncao Thushara C.Bamunuarachchige Hans‑Otto Baral D.Jayarama Bhat Janusz Błaszkowski Teun Boekhout Nattawut Boonyuen Michael Brysch‑Herzberg Bin Cao Jonathan Cazabonne Xue‑Mei Chen Claudia Coleine Dong‑Qin Dai Heide‑Marie Daniel Suzana B.G.da Silva Francisco Adriano de Souza Somayeh Dolatabadi Manish K.Dubey Arun K.Dutta Aseni Ediriweera Eleonora Egidi Mostafa S.Elshahed Xinlei Fan Juliana R.B.Felix Mahesh C.A.Galappaththi Marizeth Groenewald Li‑Su Han Bo Huang Vedprakash G.Hurdeal Anastasia N.Ignatieva Gustavo H.Jeronimo Ana L.de Jesus Serhii Kondratyuk Jaturong Kumla Martin Kukwa Qirui Li Juliana L.R.Lima Xiao‑Yong Liu Wenhua Lu H.Thorsten Lumbsch Hugo Madrid Franco Magurno Guy Marson Eric H.C.McKenzie Audrius Menkis Armin Mešić Elaine C.R.Nascimento Elena S.Nassonova Yong Nie Naasson V.L.Oliveira Emilia A.Ossowska Julia Pawłowska Ursula Peintner Igor R.Pozdnyakov Bhagya M.Premarathne A.K.Hasith Priyashantha C.Alisha Quandt Mariana B.Queiroz Kunhiraman C.Rajeshkumar Mubashar Raza Niranjan Roy Milan C.Samarakoon Alessandra A.Santos Lidiane A.Santos Felix Schumm Laura Selbmann Faruk Selcuk D.Rabern Simmons Anastasia V.Simakova Maudy Th.Smith Onden Paraparath Sruthi Nakarin Suwannarach Kazuaki Tanaka Saowaluck Tibpromma Elias O.Tomas Merve Ulukapı Nicolas Van Vooren Dhanushka N.Wanasinghe Evi Weber Qianzhen Wu Er Fu Yang Ryuichi Yoshioka Noha H.Youssef Annemarie Zandijk Gui‑Qing Zhang Jin‑Yong Zhang Heng Zhao RuiLin Zhao Oleg A.Zverkov Marco Thines Sergey A.Karpov Center for Yunnan Plateau Biological Resources Protection and Utilization College of Biological Resource and Food EngineeringQujing Normal UniversityQujing 655011YunnanPeople’s Republic of China Tropical Microbiology Research Foundation 96/N/10Meemanagoda RoadPannipitiya 10230Sri Lanka Department of Bioprocess Technology Faculty of TechnologyRajarata UniversityMihintaleSri Lanka Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiangRai 57100Thailand Innovative Institutefor Plant Health Zhongkai University of Agriculture and EngineeringGuangzhou 510225People’s Republic of China CAS Key Laboratory for Plant Biodiversity and Biogeography of East Asia(KLPB) Kunming Institute of BotanyChinese Academy of ScienceKunmingYunnanChina Belozersky Institute of Physicochemical Biology Lomonosov Moscow State UniversityLeninskye Gory1Build.40Moscow 119991Russian Federation Kharkevich Institute for Information Transmission Problems Russian Academy of SciencesMoscow 127051Russian Federation National Collection of Agricultural and Industrial Microorganisms Institute of Food Science and TechnologyHungarian University of Agriculture and Life SciencesSomlóiút 14‑161118 BudapestHungary Instituto de Biociências Universidade Federal de Mato Grosso do SulAvenida Costa e Silvas/n Bairro UniversitárioCampo GrandeMS CEP 79070‑900Brazil Instituto de Pesquisas Ambientais Secretaria do Meio AmbienteInfraestrutura e LogísticaAv.Miguel Stéfano 3687São PauloSP CEP 04301‑902Brazil Departamento de Botânica e Zoologia Universidade Federal do Rio Grande do NorteCampus UniversitárioNatalRN 59078-900Brazil All-Russian Institute of Plant Protection Sh.Podbelskogo 3PushkinSt.Petersburg 196608Russia Research Group Mycology Department of BiologyGhent University9000 GhentBelgium Faculty of Science University of South Bohemia37005ČeskéBudějoviceCzech Republic Department of Ecology and Evolutionary Biology University of Colora

Fungi are one of the most diverse groups of organisms with an estimated number of species in the range of 2–3 *** higher-level ranking of fungi has been discussed in the framework of molecular phylogenetics since Hibbett et al.,and the definition and the higher ranks(e.g.,phyla)of the‘true fungi’have been revised in several subsequent *** accumulation of novel genomic data and the advancements in phylogenetics now facilitate a robust and precise foundation for the higher-level classification within the *** study provides an updated classification of the kingdom Fungi,drawing upon a comprehensive phylogenomic analysis of Holomycota,with which we outline well-supported nodes of the fungal tree and explore more contentious *** accept 19 phyla of Fungi,***,Ascomycota,Basidiobolomycota,Basidiomycota,Blastocladiomycota,Calcarisporiellomycota,Chytridiomycota,Entomophthoromycota,Entorrhizomycota,Glomeromycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota,Mucoromycota,Neocallimastigomycota,Olpidiomycota,Rozellomycota,Sanchytriomycota,and *** the phylogenies,Caulochytriomycota resides in Chytridiomycota;thus,the former is regarded as a synonym of the latter,while Caulochytriomycetes is viewed as a class in *** provide a description of each phylum followed by its classes.A new subphylum,Sanchytriomycotina Karpov is introduced as the only subphylum in *** subclass Pneumocystomycetidae Kirk et *** Pneumocystomycetes,Ascomycota is invalid and thus *** of fossil fungi in phyla and classes are also discussed,providing examples.

关键词： Caulochytriomycota Phylogenomic analyses Pneumocystomycetidae Sanchytriomycotina

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Lipschitz continuity of solutions and corresponding multipliers to distributed and boundary semilinear elliptic optimal control problems with mixed pointwise control-state constraints

arXiv

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arXiv 2021年

作者： Nhu, V.H. Tuan, N.Q. Giang, N.B. Huong, N.T.T. Faculty of Fundamental Sciences PHENIKAA University Yen Nghia Ha Dong Hanoi12116 Viet Nam Department of Optimization and Control Theory Institute of Mathematics Vietnam Academy of Science and Technology 18 Hoang Quoc Viet road Hanoi Viet Nam Department of Mathematics Hanoi Pedagogical University 2 Xuan Hoa Vinh Phuc Phuc Yen Viet Nam Department of Information and Technology National University of Civil Engineering 55 Giai Phong Str. Hanoi Viet Nam School of Applied Mathematics and Informatics Hanoi University of Science and Technology 1 Dai Co Viet Hanoi Viet Nam

This paper is concerned with the existence and regularity of mininizers as well as of corresponding multipliers to an optimal control problem governed by semilinear elliptic equations, in which mixed pointwise control-state constraints are considered in a quite general form and the controls act simultaneously in the domain and on the boundary. Under standing assumptions, the minimizers and the corresponding multipliers do exist. Furthermore, by applying the bootstrapping technique and establishing some calculation tools for functions in Sobolev spaces of fractional order, the optimal solutions and the associated Lagrange multipliers are shown to be Lipschitz *** Codes 49K20, 35J25 Copyright © 2021, The Authors. All rights reserved.

关键词： Differential equations

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Information support of daily scrum meetings 13

Information support of daily scrum meetings

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13th International Conference on Strategic Management and Its Support by Information Systems, SMSIS 2019

作者： Ministr, Jan Pitner, Tomas Danel, Roman Chaplyha, Vyacheslav VSB - Technical University of Ostrava Faculty of Economics Department of Applied Informatics 17. Listopadu 2172/15 Ostrava-Poruba70800 Czech Republic Masaryk University Faculty of Informatics Department of Computer Systems and Communications Botanická 68a Brno Czech Republic Technical and Economic University in Českś Budějovice Faculty of Technology Department of Civil Engineering Okružní 517/10 Českś Budějovice370 01 Czech Republic Lviv National Agrarian University Department Automatic Control and Computer-Integrated Technologies 1 V. Velykoho Str. Dublyany Lviv region80381 Ukraine

ISBN: (纸本)9788024843063

The vast majority of the software developers use the agile framework for project management. Agile approach provides more flexibility and adaption to the changes against classic software development approach. The key methodology of agile approach represents the scrum. The core of scrum management forms sprint, which represents a time shift activity and envelops all other events of scrum. The outcome of the sprint should be the increment in development of the project, which is possible to submit to the customers or the users. The paper maps the possibilities of visualization of the course of individual sprints at everyday meetings of the research team (stand up meetings), which have a decisive influence on the quality of communication of the research team and the overall success of the project. © 2019 VSB-Technical University of Ostrava. All rights reserved.

关键词： Project management

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

arXiv

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arXiv 2023年

作者： Graham, Simon Vu, Quoc Dang Jahanifar, Mostafa Weigert, Martin Schmidt, Uwe Zhang, Wenhua Zhang, Jun Yang, Sen Xiang, Jinxi Wang, Xiyue Rumberger, Josef Lorenz Baumann, Elias Hirsch, Peter Liu, Lihao Hong, Chenyang Aviles-Rivero, Angelica I. Jain, Ayushi Ahn, Heeyoung Hong, Yiyu Azzuni, Hussam Xu, Min Yaqub, Mohammad Blache, Marie-Claire Piégu, Benoît Vernay, Bertrand Scherr, Tim Böhland, Moritz Löffler, Katharina Li, Jiachen Ying, Weiqin Wang, Chixin Snead, David Raza, Shan E. Ahmed Minhas, Fayyaz Rajpoot, Nasir M. Tissue Image Analytics Centre University of Warwick Coventry United Kingdom Histofy Ltd Birmingham United Kingdom Institute of Bioengineering School of Life Sciences Epfl Lausanne Switzerland Dresden Germany The Department of Computer Science The University of Hong Kong Hong Kong Tencent Ai Lab Shenzhen China College of Biomedical Engineering Sichuan University Chengdu China Department of Precision Instruments Tsinghua University Beijing China College of Computer Science Sichuan University Chengdu China Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association Berlin Germany Humboldt University of Berlin Faculty of Mathematics and Natural Sciences Berlin Germany Charité University Medicine Berlin Germany University of Bern Bern Switzerland Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Softsensor.ai BridgewaterNJ United States PRR.ai TX United States Department of R&d Center Arontier Co. Ltd Seoul Korea Republic of Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Cnrs Ifce Inrae Université de Tours Prc Nouzilly3780 France Institut de Génétique et de Biologie Moléculaire et Cellulaire Illkirch France Centre National de la Recherche Scientifique UMR7104 Illkirch France Institut National de la Santé et de la Recherche Médicale Inserm U1258 Illkirch France Université de Strasbourg Strasbourg France Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany School of Software Engineering South China University of Technology Guangzhou China Department of Pathology University Hospitals Coventry Warwickshire Nhs Trust Coventry United Kingdom Division of Biomedical Sciences Warwick Medical School University of Warwick Coventry United Kingdom

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery. © 2023, CC BY-NC-SA.

关键词： Grading

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Preface

Lecture Notes on Data Engineering and Communications Technol...

引用

Lecture Notes on Data engineering and Communications Technologies 2021年 62卷 ix-x页

作者： Balas, Valentina E. Hassanien, Aboul Ella Chakrabarti, Satyajit Mandal, Lopa Department of Automation and Applied Informatics and Department of International Relations Programs and Projects Intelligent Systems Research Centre Aurel Vlaicu University of Arad Arad Romania Department of Information Technology Faculty of Computer and Artificial Intelligence Cairo University Giza Egypt Institute of Engineering and Management Kolkata West Bengal India Department of Information Technology Institute of Engineering and Management Kolkata West Bengal India

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