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Improving Defect Inspection Quality of Deep-Learning Network in Dense Beans by Using Hough Circle Transform for Coffee Industry

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Improving Defect Inspection Quality of Deep-Learning Network...

IEEE International Conference on systems. Man and Cybernetics

作者： Cheng-Ju Kuo Ding-Chau Wang Tzu-Ting Chen Yung-Chien Chou Mao-Yuan Pai Gwo-Jiun Horng Min-Hsiung Hung Yu-Chuan Lin Tz-Heng Hsu Chao-Chun Chen Department of Computer Science and Information Engineering Institute of Manufacturing Information and Systems National Cheng Kung University (NCKU) Taiwan Dept. of Comp. Sci. & Info. Engr. Southern Taiwan University of Science and Technology (STUST) Taiwan General Research Service Center National Pingtung University of Science and Technology Taiwan Department of Computer Science and Information Engineering Chinese Culture University Taiwan

In this paper, we propose a novel Hough circle-assisting deep-network inspection scheme (HCADIS), aiming at identifying defects in dense coffee beans. The proposed HCADIS plays a critical role in a camera-based defect removal system to collect defective bean positions for picking all defects off. The idea of the HCADIS is to mix intermediate data from a deep network and a feature engineering method call Hough circle transform for utilizing advantages of both methods in inspecting beans. The Hough circle transform is adopted because it performs quite stable and bean shapes are highly close to circles in nature. A set of core mechanisms are designed for collaboration between the deep network and the Hough circle transform for precisely and accurately inspecting defective beans. Finally, we implement a prototype of the HCADIS and conduct experiments for testing the proposed scheme. The test results reveal that the HCADIS indeed successfully inspect defects among dense beans with superior performance in various metrics. This work provides industrial participants useful experiences for creating deep-learning solutions to bean products in coffee industries.

关键词： Feature extraction Transforms Inspection Industries Object detection Shape Data structures

Roadmap on Deep Learning for Microscopy

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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

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arXiv 2023年

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

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. 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

Triboelectric Backgrounds to Radio-based Polar UHE Neutrino Experiments

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arXiv 2021年

作者： Aguilar, J.A. Anker, A. Allison, P. Archambault, S. Baldi, P. Barwick, S.W. Beatty, J.J. Beise, J. Besson, Dave Bishop, A. Bondarev, E. Botner, O. Bouma, S. Buitink, S. Cataldo, M. Chen, C.C. Chen, C.H. Chen, P. Chen, Y.C. Choi, T. Clark, B.A. Clay, W. Curtis-Ginsberg, Z. Connolly, A. Cremonesi, L. Dasgupta, P. Davies, J. De Kockere, S. De Vries, K.D. Deaconu, C. DuVernois, M.A. Flaherty, J. Friedman, E. Gaior, R. Gaswint, G. Glaser, C. Hallgren, A. Hallmann, S. Ham, Y.-B. Hanson, J.C. Harty, N. Hendricks, B. Hoffman, K.D. Hong, E. Hornhuber, C. Hsu, S.Y. Hu, L. Huang, J.J. Huang, M.-H. Hughes, K. Ishihara, A. Jee, G. Jung, J. Karle, A. Kelley, J.L. Klein, S.R. Kleinfelder, S.A. Kim, J. Kim, K.-C. Kim, M.-C. Kravchenko, I. Krebs, R. Ku, Y. Kuo, C.Y. Kurusu, K. Kwon, Hyuck-Jin Lahmann, R. Landsman, H. Latif, U. Lee, C. Leung, C.-H. Li, C.-J. Liu, J. Liu, T.-C. Lu, M.-Y. Madison, K. Mammo, J. Mase, K. McAleer, S. Meures, T. Meyers, Z.S. Michaels, K. Mikhailova, M. Mulrey, K. Nam, J. Nichol, R.J. Nir, G. Nelles, A. Novikov, A. Nozdrina, A. Oberla, E. Oeyen, B. Osborn, J. Pan, Y. Pandya, H. Paul, M.P. Persichilli, C. Pfendner, C. Plaisier, I. Punsuebsay, N. Pyras, L. Rice-Smith, R. Roth, J. Ryckbosch, D. Scholten, O. Seckel, D. Seikh, M.F.H. Shiao, Y.-S. Shin, B.-K. Shultz, A. Smith, D. Southall, D. Tatar, J. Torres, J. Toscano, S. Tosi, D. Touart, J. Van Den Broeck, D.J. van Eijndhoven, N. Varner, G.S. Vieregg, A.G. Wang, M.-Z. Wang, S.-H. Wang, Y.H. Welling, C. Williams, D.R. Wissel, S. Xie, C. Yoshida, S. Young, R. Zhao, L. Zink, A. Universite Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Department of Physics and Astronomy University of California IrvineCA92697 United States Dept. of Physics Center for Cosmology and AstroParticle Physics The Ohio State University ColumbusOH43210 United States Dept. of Physics Chiba University Chiba Japan Department of Information and Computer Science University of California IrvineCA92697 United States Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden University of Kansas Department of Physics and Astronomy LawrenceKS66045 United States Dept. of Physics University of Wisconsin-Madison MadisonWI53706 United States ECAP Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen91058 Germany Vrije Universiteit Brussel Astrophysical Institute Pleinlaan 2 Brussels1050 Belgium Dept. of Physics Grad. Inst. of Astrophys. Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei Taiwan Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Dept. of Physics and Astronomy Michigan State University East LansingMI48824 United States Dept. of Physics Enrico Fermi Institue Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Dept. of Physics and Astronomy University College London London United Kingdom Vrije Universiteit Brussel Dienst ELEM Brussels Belgium Dept. of Physics University of Maryland College ParkMD20742 United States Whittier College Department of Physics WhittierCA90602 United States Dept. of Physics University of Delaware NewarkDE19716 United States Dept. of Physics Pennsylvania State University University ParkPA16802 United States Center for Multi-Messenger

The proposed IceCube-Gen2 seeks to instrument approximately 500 square kilometers of Antarctic ice near the geographic South Pole with radio antennas, in the hopes of observing the highest-energy neutrinos (E>1 EeV) populating the Universe. For this purpose IceCube-Gen2 will exploit the impulsive radio emission produced by neutrino interactions in the polar ice cap. In such polar-sited radio-frequency neutrino search experiments, rare single event candidates must be unambiguously identified above backgrounds. Background rejection strategies to date primarily target thermal noise fluctuations and also impulsive radio-frequency signals of anthropogenic origin. In this paper, we consider the possibility that ‘fake’ neutrino signals may also be generated naturally via the ‘triboelectric effect.’ This broadly describes any process in which force applied at a boundary layer results in displacement of surface charge, leading to the production of an electrostatic potential difference ∆V. Wind blowing over granular surfaces such as snow can induce such a potential difference, with subsequent coronal discharge. Discharges over timescales as short as nanoseconds can then lead to radio-frequency emissions at characteristic MHz – GHz frequencies. Using data from various past and current neutrino experiments, we find that such backgrounds are observed by all radio neutrino experiments and are generally characterized by: a) a threshold wind velocity which likely depends on the experimental signal trigger threshold and layout;for the experiments considered herein, this value is typically O(10 m/s), b) frequency spectra generally shifted to the low-end of the frequency regime to which current radio experiments are typically sensitive (100-200 MHz), c) for the strongest background signals, an apparent preference for discharges from above-surface structures, although the presence of more isotropic, lower amplitude triboelectric discharges cannot be excluded. Copyright © 2021, The Auth

关键词： Boundary layers

Towards Reproducible Bioinformatics: The OpenBio-C Scientific Workflow Environment

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Towards Reproducible Bioinformatics: The OpenBio-C Scientifi...

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Alexandros Kanterakis Galateia Iatraki Konstantina Pityanou Lefteris Koumakis Nikos Kanakaris Nikos Karacapilidis George Potamias Foundation for Research and Technology-Hellas (FORTH) Institute of Computer Science Heraklion Greece Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Industrial Management and Information Systems Lab MEAD University of Patras Patras Greece

ISBN: (纸本)9781728146188

The highly competitive environment of post-genomics biomedical research is pushing towards the production of highly qualified results and their utilization in clinical practice. Due to this pressure, an important factor of scientific progress has been underestimated namely, the reproducibility of the results. To this end, it is critical to design and implement computational platforms that enable seamless and unhindered access to distributed bio-data, software and computational infrastructures. Furthermore, the need to support collaboration and form synergistic activities between the engaged researchers is also raised, as the mean to achieve consensus on respective scientific discoveries. The aforementioned needs and requirements underlie the motivations for the development of OpenBio-C workflow environment, and determine its fundamental objectives. The insufficiencies of current workflow editing and execution environments are explored, and the key-directions to advance over an environment that supports reproducibility are stated. The basic components and functionality of the OpenBio-C initial implementation (beta version) are detailed.

关键词： Bioinformatics Tools Collaboration Pipelines Genomics Market research Semantics

Towards Self-Explainable Cyber-Physical systems

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Towards Self-Explainable Cyber-Physical Systems

ACM/IEEE International Conference on Model Driven engineering Languages and systems.Companion

作者： Mathias Blumreiter Joel Greenyer Francisco Javier Chiyah Garcia Verena Klos Maike Schwammberger Christoph Sommer Andreas Vogelsang Andreas Wortmann Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universitat Hannover Heriot-Watt University Software and Embedded Systems Engineering Technische Universitat Berlin Department of Computing Science University of Oldenburg Heinz Nixdorf Institute and Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universitat Berlin Software Engineering RWTH Aachen University

ISBN: (纸本)9781728151267

With the increasing complexity of Cyber-Physical systems. their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems.that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems. we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems.that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.

关键词： Explainability Self-adaptive systems Cyber-physical systems Adaptive systems Cyber-physical systems MONITOR Runtime Buildings

Multi-lingual dialogue act recognition with deep learning methods

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arXiv 2019年

作者： Martínek, Jiří Král, Pavel Lenc, Ladislav Cerisara, Christophe Dept. of Computer Science and Engineering University of West Bohemia Plzen Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Plzen Czech Republic LORIA UMR 7503 BP 239 Vandoeuvre54506 France

This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Underrepresented Groups Threats to Belonging in Computing

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Underrepresented Groups Threats to Belonging in Computing

Research in Equity and Sustained Participation in engineering, Computing, and Technology (RESPECT)

作者： Marlon Mejias Ketly Jean-Pierre Gloria Washington Legand Burge Dept. of Software & Information Systems University. of North Carolina at Charlotte Charlotte NC USA Post-Doctoral Fellow - Process Data Educcational Testing Services Princeton NJ USA Dept. of Electrical Engineering & Computer Science Howard Unversity Washingington D.C. USA

Increasing the number of students exposed to computer science (CS) may not increase throughput in the pipeline if barriers to retention are not addressed. One major barrier is student's perception of belonging. To begin creating theoretical grounds for a common discourse to successfully matriculating underrepresented students, and retaining them in the field, threats to their belonging (barriers to retention) and how a survey of literature suggest they impact CS are presented.

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Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

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

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

关键词： computer vision