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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： image segmentation

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A Temporally and Spatially Explicit, Mechanistic Estimation of Ragweed Pollen Concentrations Across Europe

SSRN

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SSRN 2023年

作者： Makra, László Matyasovszky, István Tusnády, Gábor Ziska, Lewis H. Hess, Jeremy J. Nyúl, László G. Chapman, Daniel S. Coviello, Luca Gobbi, Andrea Jurman, Giuseppe Furlanello, Cesare Brunato, Mauro Damialis, Athanasios Charalampopoulos, Athanasios Müller-Schärer, Heinz Schneider, Norbert Szabó, Bence Sümeghy, Zoltán Páldy, Anna Magyar, Donát Bergmann, Karl-Christian Deák, Áron József Mikó, Edit Thibaudon, Michel Oliver, Gilles Albertini, Roberto Bonini, Maira Šikoparija, Branko Radišić, Predrag Mitrović Josipović, Mirjana Gehrig, Regula Severova, Elena Rodinkova, Victoria Palamarchuk, Olena Stjepanović, Barbara Ianovici, Nicoleta Berger, Uwe Kofol Seliger, Andreja Rybníček, Ondřej Myszkowska, Dorota Dąbrowska-Zapart, Katarzyna Majkowska-Wojciechowska, Barbara Weryszko-Chmielewska, Elzbieta Grewling, Lukasz Rapiejko, Piotr Malkiewicz, Malgorzata Šaulienė, Ingrida Shalaboda, Valentina Prykhodo, Olexander Maleeva, Anna Ščevková, Jana Bullock, James M. Institute of Economics and Rural Development Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Department of Meteorology Eötvös Loránd University P.O.B. 32 Budapest1518 Hungary Alfréd Rényi Institute of Mathematics P.O.B 127 Budapest1364 Hungary Mailman School of Public Health Columbia University New YorkNY10032 United States Department of Global Health University of Washington SeattleWA98105 United States Department of Image Processing and Computer Graphics University of Szeged P.O.B. 652 Szeged6701 Hungary Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling Scotland StirlingFK9 4LA United Kingdom University of Trento Enogis s.r.l. Trento Italy Bruno Kessler Foundation Trento Italy HK3 Lab Orobix Life Srl Rovereto Bergamo Italy Department of Information Engineering and Computer Science University of Trento Trento Italy Terrestrial Ecology and Climate Change Department of Ecology School of Biology Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Departement of Biology Unit of Ecology and Evolution University of Fribourg FribourgCH-1700 Switzerland National Institute of Environmental Health Albert Flórián út 2-6 Budapest1097 Hungary Institute of Allergology Charité Hindenburgdamm 30 Berlin12203 Germany Institute of Animal Science and Wildlife Management Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Réseau National de Surveillance Aérobiologique 11 chemin de la Creuzille Le Plat du Pin Brussieu696905 France Laboratory of Hygiene and Aerobiology Department of Medicine and Surgery University of Parma U.O. Medicina Interna di Continuità Azienda Ospedaliero-Universitaria di Parma Via Gramsci 14 Parma43126 Italy Hygiene and Public Health Service via Spagliardi 19 Parabiago Milan20015 Italy BioSensе Institute Research Institute for Information Technologies in Biosystems University of Novi Sad Dr

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant’s reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating annual total pollen concentrations than those of the GM approach. These are the first published modeled maps using altitude correction and phenology to recover missing pollen information. We created a web page (http://***/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions. © 2023, The Authors. All rights reserved.

关键词： Climate change

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： image segmentation

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Network Resilience

arXiv

引用

arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study

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The Lancet. Digital health 2025年第5期7卷 100862页

作者： Jirong Yi Anna M Marcinkiewicz Aakash Shanbhag Robert J H Miller Jolien Geers Wenhao Zhang Aditya Killekar Nipun Manral Mark Lemley Mikolaj Buchwald Jacek Kwiecinski Jianhang Zhou Paul B Kavanagh Joanna X Liang Valerie Builoff Terrence D Ruddy Andrew J Einstein Attila Feher Edward J Miller Albert J Sinusas Daniel S Berman Damini Dey Piotr J Slomka Departments of Medicine (Division of Artificial Intelligence in Medicine) Biomedical Sciences and Imaging Cedars-Sinai Medical Center Los Angeles CA USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Biomedical Sciences and Imaging Cedars-Sinai Medical Center Los Angeles CA USA Center of Radiological Diagnostics National Medical Institute of the Ministry of the Interior and Administration Warsaw Poland. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA. Department of Cardiac Sciences University of Calgary Calgary AB Canada. Department of Cardiology Centrum voor Hart-en Vaatziekten (CHVZ) Universitair Ziekenhuis Brussel (UZ Brussel) Vrije Universiteit Brussel (VUB) Brussels Belgium. Department of Interventional Cardiology and Angiology Institute of Cardiology Warsaw Poland. Division of Cardiology University of Ottawa Heart Institute Ottawa ON Canada. Division of Cardiology Department of Medicine and Department of Radiology Columbia University Irving Medical Center and New York-Presbyterian Hospital New York NY USA. Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New Haven CT USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Biomedical Sciences and Imaging Cedars-Sinai Medical Center Los Angeles CA USA. Electronic address: piotr.slomka@***.

BACKGROUND:CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification. METHODS:We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves. FINDINGS:The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<

关键词： image segmentation

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

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

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BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

arXiv

引用

arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Zapaishchykova, Anna Pavaine, Julija Shah, Lubdha M. Jones, Blaise V. Sheth, Nakul Prabhu, Sanjay P. McAllister, Aaron S. Tu, Wenxin Nandolia, Khanak K. Rodriguez, Andres F. Shaikh, Ibraheem Salman Sanchez-Montano, Mariana Lai, Hollie Anne Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anderson, Hannah Anwar, Syed Muhammed Aristizabal, Alejandro Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Eddy, James Ezhov, Ivan Familiar, Ariana M. Farahani, Keyvan Gandhi, Deep Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Elaine, Elaine Karargyris, Alexandros Kassem, Hasan Khalili, Neda Kofler, Florian LaBella, Dominic Van Leemput, Koen Li, Hongwei B. Maleki, Nazanin Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Pati, Sarthak Piraud, Marie Poussaint, Tina Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Sheller, MIcah Shinohara, Russell Takeshi Viswanathan, Karthik Wang, Chunhao Wiestler, Benedikt Wiggins, Walter F. Davatzikos, Christos Storm, Phillip B. Bornhorst, Miriam Packer, Roger Hummel, Trent de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Ware, Jeffrey B. Kann, Benjamin H. Rood, Brian Resnick, Adam Bakas, Spyridon Vossough, Arastoo Linguraru, Marius George The Children’s Hospital of Philadelphia PA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Dana-Farber Brigham Cancer Center Boston Children’s Hospital BostonMA United States Department of Paediatric Radiology Royal Manchester Children’s Hospital Manchester University Hospitals NHS Foundation Trust University of Manchester Manchester United Kingdom University of Utah UT United States Cincinnati Children’s Hospital Medical Center OH United States Department of Radiology The Children’s Hospital of Philadelphia PA United States Division of Neuroradiology Department of Radiology Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Radiology Nationwide Children’s Hospital OH United States College of Arts and Sciences University of Pennsylvania PA United States Department of Diagnostic and Interventional Radiology All India Institute of Medical Sciences Rishikesh India Department of Diagnostic & Interventional Imaging Neuroradiology Section The University of Texas Health HoustonTX United States Beth Israel Deaconess Medical Center Harvard Medical School MA United States Unidad de Patologia Clinica:Guadalajara Jalisco Mexico Department of Radiology Children’s Health Orange County CA United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States MLCommons United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors. © 2024, CC BY.

关键词： Pediatrics

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