检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

WCE 2010 - World Congress on Engineering 2010: Preface

WCE 2010 - World Congress on Engineering 2010

引用

WCE 2010 - World Congress on Engineering 2010 2010年 1卷 l页

作者： Korsunsky, Alexander M. Hunter, Andrew Hukins, David W.L. Gelman, Len Hogger, Christopher John Ceglarek, Darek J. Payne, Stephen Ao, S.I. Ahmad, Munir Alexandrou, Ioannis Al-Nuaimy, Waleed Amavasai, Bala P. An, Yoon-Young Ariwa, Ezendu Arteche, Josu Audrino, Francesco Ayesh, Aladdin Baber, Chris Bailey, Colin Balkan, Naci Barria, Javier Bartosova, Jitka Benkrid, Khaled Bleijs, Hans Bluck, Michael Bose, Indranil Bouzas, Paula R. Braiden, Paul M. Brdys, Mietek Burriesci, Gaetano Cannataro, Mario Carvalho, Alexandre Chang, Chin-Chen Chen, Dechang Chen, Guanghui George Chen, Yuh-Shyan Chiclana, Francisco Cooke, Alastair Das, Diganta Bhusan Davis, Darryl N. Dayoub, Iyad Raman, Suash Deb C. V. Demetriou, Ioannis C. Devai, Frank Dilmaghani, Reza S. Dini, Daniele Drikakis, Dimitris Durkan, Colm Durodola, John Etebar, Kamran Fenn, Peter Figueiredo, Adelaide Florou, Giannoula Freear, Steven Gabrys, Bogdan Galbraith, Graham H. Gaskell, Philip H. Gaura, Elena Ge, Zhao-Qiang Ghafouri-Shiraz, H. Ghavami, Mohammad Giannopoulos, Kostas Pruneda Gonzalez, Rosa Eva Gracia, Angel Marin Grecos, Christos Guan, Lin Gulpinar, Nalan Guo, Renkuan Guo, Yakun Hardalupas, Y. He, Li Herrero, Josep R. Hicks, Ben James Hines, Evor L. Hodgson, Simon Horsfall, Alton Hosein, Patrick Hu, Fun Hu, Osheng Ijomah, Winifred Ming, Ji James, Anne Jancovic, Peter Jhumka, Arshad Kamareddine, Fairouz Kannan, Rajgopal Karsligil, M. Elif Katircioglu, Salih Turan Khalid, Allali Kokossis, Antonis Kontis, Konstantinos Kulekci, M. Oguzhan Laukaitis, Algirdas Leeson, Mark Limbachiya, Mukesh C. Li, Layuan Li, Lin Lin, Phone Ling, Wing-Kuen Macias Lopez, Elsa M. Lovas, Terese Luglio, Michele Mainardi, Stefano Mahanti, Prabhat K. Marinos, Ioannides Maropoulos, Paul Mativenga, Paul Mavrommatis, George Meintanis, S.G. Meisel, Yezid Donoso Mekid, Samir Mias, Christos Morin, Annie Musliu, Nysret Nabhani, Farhad Naguib, Raouf Olatunbosun, Oluremi Olsen, Sarah Ozalp, A. Alper Pang, Ai-Chun Pao, William K.-S. Piera, Miquel Angel Pinciroli, Francesco Popescu, Theodor D. Power Trinity College Department of Engineering Science University of Oxford United Kingdom Vision and AI Research Group Lincoln University United Kingdom Department of Computing and Informatics Lincoln University United Kingdom Department of Bio-medical Engineering University of Birmingham United Kingdom Department of Mechanical and Manufacturing Engineering University of Birmingham United Kingdom Department of Vibro-Acoustic Monitoring United Kingdom Condition Monitoring Technical Committee British Institute of NDT United Kingdom Centre of Vibro-Acoustics and Fatigue United Kingdom Cranfield University United Kingdom Department of Computing Imperial College London United Kingdom International Manufacturing Centre University of Warwick United Kingdom Department of Industrial and Systems Engineering University of Wisconsin-Madison United States University Lecturer in BioMedical Engineering United Kingdom Degrees Keble College Department of Engineering Science University of Oxford United Kingdom Oxford University Computing Laboratory University of Oxford United Kingdom Harvard School of Engineering and Applied Sciences Harvard University United States University of Teesside United Kingdom University of Liverpool United Kingdom Sheffield Hallam University United Kingdom Electronics and Telecommunications Research Institute Korea Republic of London Metropolitan University United Kingdom Universidad del Pais Vasco Euskal Herriko Unibertsitatea Spain University of St. Gallen Switzerland De Montfort University United Kingdom University of Birmingham United Kingdom University of Manchester United Kingdom University of Essex United Kingdom Imperial College London United Kingdom University of Economics Czech Republic University of Edinburgh United Kingdom University of Leicester United Kingdom University of Hong Kong Hong Kong Hong Kong University of Granada Spain Newcastle University United Kingdom University College London United Kingdom University Magna Graecia of Catanza

来源：评论

学校读者我要写书评

暂无评论

The Healthgrid White Paper 3

The Healthgrid White Paper

引用

3rd Annual Conference of the Healthgrid Association, Healthgrid 2005

作者： Breton, Vincent Dean, Kevin Solomonides, Tony Aloisio, Giovanni Benkner, Siegfried Bilofsky, Howard Blanquer, Ignacio Brady, Michael Cannataro, Mario Chouvarda, Ioanna Claerhout, Brecht De Moor, Georges De Neve, Wilfried De Wagter, Carlos Fiore, Sandro Hassan, Kinda Heeren, Germaine Hernández, Vicente Herveg, Jean A.M. Hofmann, Martin Jones, Chris Koutkias, Vassilios Lloyd, Sharon Lonsdale, Guy Alonso, Victoria L. Maglaveras, Nicos Maigne, Lydia Malousi, Andigoni Martín-Sánchez, Fernando McClatchey, Richard Medico, Enzo Miguet, Serge Mirto, Maria Montagnat, Johan Nørager1, Sofie Nozaki, Kazunori Oliveira, Ilídio Castro Pennec, Xavier Poullet, Yves Merino, Juan Pedro Sánchez Strizh, Irina G. Taillet, Michel Tristram, Clive Tverdokhlebov, Nikolay Veltri, Pierangelo Ziegler, René CNRS-IN2P3 LPC Campus des Cézeaux AubiereCedex63177 France Internet Business Solutions Group Cisco Systems 9 New Square Bedfont Lakes Middlesex FelthamTW14 8HA United Kingdom University of the West of England Bristol CEMS Coldharbour Lane BristolBS6 6TH United Kingdom Center for Advanced Computational Technologies ISUFI Dept. of Innovation Engineering University of Lecce Via per Monteroni Lecce73100 Italy Institute of Scientific Computing University of Vienna Nordbergstrasse 15 ViennaA-1090 Austria University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Center for Bioinformatics 1416 Blockley Hall 423 Guardian Drive PhiladelphiaPA19104-6021 United States Universidad Politecnica de Valencia Camino de Vera/n Valencia46022 Spain FRS FREng Dept. of Engineering Science Oxford University Parks Road OxfordOX1 3PJ United Kingdom Magna Graecia University of Catanzaro School of Bioinformatics and Biomedical Engineering Campus di Germaneto Viale Europa Germaneto Catanzaro88100 Italy Aristotle University The Medical School Lab of Medical Informatics Box 323 Thessaloniki54124 Greece Custodix NV Verlorenbroodstraat 120 Bus 14 MerelbekeB-9820 Belgium Research in Advanced Medical Informatics and Telematics UZ Gent 5K3 De Pintelaan 185 GentB9000 Belgium Department of Radiotherapy Ghent University Hospital Building P7 De Pintelaan 185 GentB-9000 Belgium Laboratoire LIRIS Université Lumière Lyon 2 Campus Porte des Alpes 5 avenue Pierre Mendès France Bron69676 France av. E.Mounierlaan 83 Brussels1200 Belgium Faculté de Droit de Namur - FUNDP Centre de Recherches Informatiques & Droit 5 rempart de la Vierge Namur5000 Belgium Schloss Birlinghoven Sankt Augustin53754 Germany CERN Geneva 231211 Switzerland Oxford University Computing Laboratory Wolfson Building OxfordOX1 3QD United Kingdom C&C Research Laboratories NEC Europe Ltd. Rathausallee 10 Sankt AugustinD-53757 Germany Medic

ISBN: (纸本)158603510X

Over the last four years, a community of researchers working on Grid and High Performance computing technologies started discussing the barriers and opportunities that grid technologies must face and exploit for the development of health-related applications. This interest lead to the first Healthgrid conference, held in Lyon, France, on January 16th–17th, 2003, with the focus of creating increased awareness about the possibilities and advantages linked to the deployment of grid technologies in health, ultimately targeting the creation of a European/international grid infrastructure for health. The topics of this conference converged with the position of the eHealth division of the European Commission, whose mandate from the Lisbon Meeting was "To develop an intelligent environment that enables ubiquitous management of citizens’ health status, and to assist health professionals in coping with some major challenges, risk management and the integration into clinical practice of advances in health knowledge." In this context "Health" involves not only clinical procedures but covers the whole range of information from molecular level (genetic and proteomic information) over cells and tissues, to the individual and finally the population level (social healthcare). Grid technology offers the opportunity to create a common working backbone for all different members of this large "health family" and will hopefully lead to an increased awareness and interoperability among disciplines. The first HealthGrid conference led to the creation of the Healthgrid association, a non-profit research association legally incorporated in France but formed from the broad community of European researchers and institutions sharing expertise in health grids. After the second Healthgrid conference, held in Clermont-Ferrand on January 29th–30th, 2004, the need for a "white paper" on the current status and prospective of health grids was raised. Over fifty experts from different areas of grid tec

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Why is the winner the best?

arXiv

引用

arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M.D. Isensee, F. Adler, T.J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Rädsch, T. Rafael-Patiño, J. Bawa, V. Singh Speidel, S. Sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martinčič, T. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Štepec, D. Subedi, R. Tochon, G. Torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-Schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Search for heavy resonances decaying to a Z boson and a photon in pp collisions at s=13 TeV with the ATLAS detector

引用

Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2017年 764卷 11-30页

作者： Aaboud, M. Aad, G. Abbott, B. Abdallah, J. Abdinov, O. Abeloos, B. Aben, R. AbouZeid, O.S. Abraham, N.L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B.S. Adachi, S. Adamczyk, L. Adams, D.L. Adelman, J. Adomeit, S. Adye, T. Affolder, A.A. Agatonovic-Jovin, T. Agricola, J. Aguilar-Saavedra, J.A. Ahlen, S.P. Ahmadov, F. Aielli, G. Akerstedt, H. Åkesson, T.P.A. Akimov, A.V. Alberghi, G.L. Albert, J. Albrand, S. Alconada Verzini, M.J. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S.P. Allbrooke, B.M.M. Allen, B.W. Allport, P.P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A.A. Alstaty, M. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M.G. Amadio, B.T. Amako, K. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amorim, A. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L.S. Andari, N. Andeen, T. Anders, C.F. Anders, G. Anders, J.K. Anderson, K.J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Anger, P. Angerami, A. Anghinolfi, F. Anisenkov, A.V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J.P. Arce, A.T.H. Arduh, F.A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A.J. Armitage, L.J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Åsman, B. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N.B. Augsten, K. Avolio, G. Axen, B. Ayoub, M.K. Azuelos, G. Baak, M.A. Baas, A.E. Baca, M.J. Bachacou, H. Bachas, K. Backes, M. Backhaus, M. Bagiacchi, P. Bagnaia, P. Bai, Y. Baines, J.T. Baker, O.K. Baldin, E.M. Balek, P. Balestri, T. Balli, F. Balunas, W.K. Banas, E. Banerjee, Sw. Bannoura, A.A.E. Barak, L. Barberio, E.L. Barberis, D. Barbero, M. Barillari, T. Barisits, M-S Barklow, T. Barlow, N. Barnes, S.L. Barnett, B.M. Barnett, R.M. Barnovska-Blenessy, Z. Baroncelli, A. Barone, G. Barr, A.J. Barranco Navarro, L. Barreiro, F. Barreiro Gui Department of Physics University of Adelaide Adelaide Australia Physics Department SUNY Albany Albany NY United States Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Istanbul Aydin University Istanbul Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP CNRS/IN2P3 Université Savoie Mont Blanc Annecy-le-Vieux France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics The University of Texas at Arlington Arlington TX United States Physics Department University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics The University of Texas at Austin Austin TX United States Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Institut de Física d'Altes Energies (IFAE) The Barcelona Institute of Science and Technology Barcelona Spain Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory University of California Berkeley CA United States Department of Physics Humboldt University Berlin Germany Albert Einstein Center for Fundamental Physics Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Centro de Investigaciones Universidad Antonio Narino Bogota Colombia INFN Sezione di Bologna Italy Dipartimento di Fis

This Letter presents a search for new resonances with mass larger than 250 GeV, decaying to a Z boson and a photon. The dataset consists of an integrated luminosity of 3.2 fb−1 of pp collisions collected at s=13 TeV w... 详细信息

This Letter presents a search for new resonances with mass larger than 250 GeV, decaying to a Z boson and a photon. The dataset consists of an integrated luminosity of 3.2 fb⁻¹ of pp collisions collected at s=13 TeV with the ATLAS detector at the Large Hadron Collider. The Z bosons are identified through their decays either to charged, light, lepton pairs (e⁺e⁻, μ⁺μ⁻) or to hadrons. The data are found to be consistent with the expected background in the whole mass range investigated and upper limits are set on the production cross section times decay branching ratio to Zγ of a narrow scalar boson with mass between 250 GeV and 2.75 TeV. © 2016 The Author(s)

关键词： Z bosons CROSS sections (Nuclear physics) PHOTON-photon interactions LARGE Hadron Collider (France & Switzerland) DILEPTON production NUCLEAR counters

来源：评论

学校读者我要写书评

暂无评论

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

引用

Computers in Biology and Medicine 2024年 176卷 108588-108588页

作者： Gómez-Pascual, Alicia Naccache, Talel Xu, Jin Hooshmand, Kourosh Wretlind, Asger Gabrielli, Martina Lombardo, Marta Tiffany Shi, Liu Buckley, Noel J. Tijms, Betty M. Vos, Stephanie J.B. ten Kate, Mara Engelborghs, Sebastiaan Sleegers, Kristel Frisoni, Giovanni B. Wallin, Anders Lleó, Alberto Popp, Julius Martinez-Lage, Pablo Streffer, Johannes Barkhof, Frederik Zetterberg, Henrik Visser, Pieter Jelle Lovestone, Simon Bertram, Lars Nevado-Holgado, Alejo J. Gualerzi, Alice Picciolini, Silvia Proitsi, Petroula Verderio, Claudia Botía, Juan A. Legido-Quigley, Cristina Department of Information and Communications Engineering Faculty of Informatics University of Murcia Murcia Spain Steno Diabetes Center Copenhagen Herlev Denmark Department of Data Science City University of London United Kingdom Institute of Pharmaceutical Science King's College London London United Kingdom CNR Institute of Neuroscience Vedano al Lambro20854 Italy School of Medicine and Surgery University of Milano-Bicocca 20126 Italy Oxford United Kingdom Department of Psychiatry University of Oxford United Kingdom Kavli Institute for Nanoscience Discovery Denmark Alzheimer Center VU University Medical Center Amsterdam Netherlands Department of Psychiatry and Neuropsychology Alzheimer Centrum Limburg Maastricht University Maastricht Netherlands Department of Biomedical Sciences University of Antwerp Antwerp Belgium Vrije Universiteit Brussel Brussels Belgium Complex Genetics Group VIB Center for Molecular Neurology VIB Antwerp Belgium Institute Born-Bunge Department of Biomedical Sciences University of Antwerp Antwerp Belgium University of Geneva Geneva Switzerland IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Institute of Neuroscience and Physiology Sahlgrenska Academy at University of Gothenburg Gothenburg Sweden Barcelona Spain Old age psychiatry University Hospital of Lausanne University of Lausanne Switzerland Department of Geriatric Psychiatry University Hospital of Psychiatry Zürich University of Zürich Switzerland CITA-Alzheimer Foundation San Sebastian Spain AC Immune SA formerly Janssen R&D LLC. Beerse Belgium at the time of study conduct Lausanne Switzerland Department of Radiology and Nuclear Medicine Amsterdam UMC Vrije Universiteit Netherlands Queen Square Institute of Neurology and Centre for Medical Image Computing University College London United Kingdom Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Mölndal Sweden Department of Psychiatry and Neurochemistry Institute of

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways. © 2024 The Authors

关键词： Metabolites

来源：评论

学校读者我要写书评

暂无评论

Understanding metric-related pitfalls in image analysis validation

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：