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Metrics reloaded: recommendations for image analysis validation
L. Maier-Hein, A. Reinke, P. Godau, MD. Tizabi, F. Buettner, E. Christodoulou, B. Glocker, F. Isensee, J. Kleesiek, M. Kozubek, M. Reyes, MA. Riegler, M. Wiesenfarth, AE. Kavur, CH. Sudre, M. Baumgartner, M. Eisenmann, D. Heckmann-Nötzel, T....
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, přehledy
Grantová podpora
P41 GM135019
NIGMS NIH HHS - United States
NLK
ProQuest Central
od 2004-10-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2004-10-01 do Před 1 rokem
- MeSH
- algoritmy * MeSH
- počítačové zpracování obrazu * MeSH
- sémantika MeSH
- strojové učení MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland
BCN Medtech Universitat Pompeu Fabra Barcelona Spain
Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany
Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA
Center for Scalable Data Analytics and Artificial Intelligence Leipzig University Leipzig Germany
Center for Systems Biology Dresden Germany
Centre for Intelligent Machines and MILA McGill University Montréal Quebec Canada
Centre for Medical Image Computing University College London London UK
Centre for Statistics in Medicine University of Oxford Nuffield Orthopaedic Centre Oxford UK
Department AIBE Friedrich Alexander Universität Erlangen Nürnberg Germany
Department of Biomedical Data Sciences Leiden University Medical Center Leiden the Netherlands
Department of Biomedical Informatics Stony Brook University Health Science Center Stony Brook NY USA
Department of Computer Science IT University of Copenhagen Copenhagen Denmark
Department of Computer Science UiT The Arctic University of Norway Tromsø Norway
Department of Computer Science University of Toronto Toronto Ontario Canada
Department of Computing Faculty of Engineering Imperial College London London UK
Department of Computing Imperial College London South Kensington Campus London UK
Department of Development and Regeneration and EPI centre KU Leuven Leuven Belgium
Department of Digital Medical Technologies Holon Institute of Technology Holon Israel
Department of Informatics Goethe University Frankfurt Frankfurt am Main Germany
Department of Medical Biophysics University of Toronto Toronto Ontario Canada
Department of Medicine Goethe University Frankfurt Frankfurt am Main Germany
Department of Pathology Radboud University Medical Center Nijmegen the Netherlands
Department of Quantitative Biomedicine University of Zurich Zurich Switzerland
Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland
Department of Surgery Perelman School of Medicine Philadelphia PA USA
Department of Surgery University Health Network Philadelphia PA USA
Digital Engineering Faculty University of Potsdam Potsdam Germany
Electrical Engineering Vanderbilt University Nashville TN USA
European Federation for Medical Informatics Le Mont sur Lausanne Switzerland
Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany
Faculty of Medicine Heidelberg University Hospital Heidelberg Germany
Frankfurt Cancer Insititute Frankfurt am Main Germany
Fraunhofer MEVIS Bremen Germany
German Cancer Research Center Heidelberg Division of Biostatistics Heidelberg Germany
German Cancer Research Center Heidelberg Division of Intelligent Medical Systems Heidelberg Germany
German Cancer Research Center Heidelberg Division of Medical Image Computing Heidelberg Germany
German Cancer Research Center Heidelberg Heidelberg Germany
German Cancer Research Center Heidelberg HI Applied Computer Vision Lab Heidelberg Germany
German Cancer Research Center Heidelberg HI Helmholtz Imaging Heidelberg Germany
German Cancer Research Center Heidelberg Interactive Machine Learning Group Heidelberg Germany
Google 1600 Amphitheatre Pkwy Mountain View CA USA
Google Health DeepMind London UK
Google Health Google Palo Alto CA USA
Helmholtz AI Oberschleißheim Germany
IHU Strasbourg Strasbourg France
Imaging Platform Broad Institute of MIT and Harvard Cambridge MA USA
Informatics Institute Faculty of Science University of Amsterdam Amsterdam the Netherlands
Information Systems Institute University of Applied Sciences Western Switzerland Sierre Switzerland
Institute for AI in Medicine University Medicine Essen Essen Germany
Institute for Computational Biomedicine Heidelberg University Heidelberg Germany
Institute of Information Systems Engineering TU Wien Vienna Austria
Instituto de Cálculo CONICET Universidad de Buenos Aires Buenos Aires Argentina
Laboratoire Traitement du Signal et de l'Image UMR_S 1099 Université de Rennes 1 Rennes France
Medical Faculty Heidelberg University Heidelberg Germany
Medical Faculty University of Geneva Geneva Switzerland
National Institutes of Health Clinical Center Bethesda MD USA
Neurocenter Oulu Oulu University Hospital Oulu Finland
Parietal project team INRIA Saclay Île de France Palaiseau France
Physical Sciences Sunnybrook Research Institute Toronto Ontario Canada
Princess Margaret Cancer Centre University Health Network Toronto Ontario Canada
Radboud Institute for Health Sciences Radboud University Medical Center Nijmegen the Netherlands
Research Unit of Health Sciences and Technology Faculty of Medicine University of Oulu Oulu Finland
School of Biomedical Engineering and Imaging Science King's College London London UK
School of Engineering The University of Edinburgh Edinburgh Scotland
Simula Metropolitan Center for Digital Engineering Oslo Norway
Technische Universität Dresden DFG Cluster of Excellence 'Physics of Life' Dresden Germany
Tissue Image Analytics Laboratory Department of Computer Science University of Warwick Coventry UK
Vector Institute for Artificial Intelligence Toronto Ontario Canada
Citace poskytuje Crossref.org
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