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Understanding metric-related pitfalls in image analysis validation

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  • معلومة اضافية
    • Contributors:
      German Cancer Research Center - Deutsches Krebsforschungszentrum Heidelberg (DKFZ); University College of London London (UCL); Consejo Nacional de Investigaciones Científicas y Técnicas Buenos Aires (CONICET); King‘s College London; Institut québécois d’intelligence artificielle = Quebec Artificial Intelligence Institute (Mila); McGill University = Université McGill Montréal, Canada; Indiana University School of Medicine; Indiana University System; Holon Institut of Technology (HIT); Department of Electrical Engineering KU Leuven (KU-ESAT); Catholic University of Leuven = Katholieke Universiteit Leuven (KU Leuven); German Cancer Consortium Heidelberg (DKTK); IT University of Copenhagen (ITU); Leibniz Institute for Analytical Sciences (ISAS); Broad Institute Cambridge; Harvard University-Massachusetts Institute of Technology (MIT); University of Oxford; National Cancer Institute Bethesda (NCI-NIH); National Institutes of Health Bethesda, MD, USA (NIH); Universitat Pompeu Fabra Barcelona (UPF); Fraunhofer Institute for Digital Medicine (Fraunhofer MEVIS); Fraunhofer (Fraunhofer-Gesellschaft); Imperial College London; Leipzig University / Universität Leipzig; Perelman School of Medicine; University of Pennsylvania; University of Toronto; Radboud University Medical Center Nijmegen; Laboratoire Traitement du Signal et de l'Image (LTSI); Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM); University of Potsdam = Universität Potsdam; L'Institut hospitalo-universitaire de Strasbourg (IHU Strasbourg); Les Hôpitaux Universitaires de Strasbourg (HUS)-Institut National de Recherche en Informatique et en Automatique (Inria)-l'Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD)-La Fédération des Crédits Mutuels Centre Est (FCMCE)-L'Association pour la Recherche contre le Cancer (ARC)-La société Karl STORZ; Google Inc; Research at Google; Heidelberg University Hospital Heidelberg; University Hospital Essen (AöR); Helmholtz AI; Seoul National University Seoul (SNU); Masaryk University Brno (MUNI); EMBL Heidelberg; Stony Brook University SUNY (SBU); State University of New York (SUNY); Vanderbilt University Nashville; Radboud University Nijmegen; University Health Network Toronto, ON, Canada; Sunnybrook Research Institute Toronto (SRI); Sunnybrook Health Sciences Centre Toronto (Sunnybrook); University of New South Wales Sydney (UNSW); Universität Zürich Zürich = University of Zurich (UZH); Universiteit Utrecht / Utrecht University Utrecht; Université de Genève = University of Geneva (UNIGE); Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf Hamburg (UKE); Allen Institute for cell sciences; University of Warwick Coventry; Universität Bern = University of Bern = Université de Berne (UNIBE); Simula Research Laboratory OSLO; NVIDIA (NVIDIA); Universität Heidelberg Heidelberg = Heidelberg University; University of Amsterdam Amsterdam = Universiteit van Amsterdam (UvA); Vienna University of Technology = Technische Universität Wien (TU Wien); University of Oulu; University of Edinburgh (Edin.); Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données (SODA); Inria Saclay - Ile de France; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria); National Institute of Allergy and Infectious Diseases Bethesda (NIAID-NIH); ANR-10-IAHU-0002,MIX-Surg,Institut de Chirurgie Mini-Invasive guidée par l'Image(2010)
    • بيانات النشر:
      HAL CCSD
    • الموضوع:
      2023
    • Collection:
      Université de Rennes 1: Publications scientifiques (HAL)
    • نبذة مختصرة :
      arXiv admin note: text overlap with arXiv:2104.05642 ; Validation metrics are key for the reliable tracking of scientific progress and for 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 in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: 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. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.
    • Relation:
      info:eu-repo/semantics/altIdentifier/arxiv/2302.01790; ARXIV: 2302.01790
    • الدخول الالكتروني :
      https://hal.science/hal-04345927
      https://hal.science/hal-04345927v1/document
      https://hal.science/hal-04345927v1/file/2302.01790.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.921FE823