Understanding metric-related pitfalls in image analysis validation.
Biological Imaging
Biomedical Image Processing
Challenges
Classification
Computer Vision
Detection
Evaluation
Good Scientific Practice
Instance Segmentation
Localization
Medical Imaging
Metrics
Pitfalls
Segmentation
Semantic Segmentation
Validation
Journal
ArXiv
ISSN: 2331-8422
Titre abrégé: ArXiv
Pays: United States
ID NLM: 101759493
Informations de publication
Date de publication:
25 Sep 2023
25 Sep 2023
Historique:
pubmed:
23
3
2023
medline:
23
3
2023
entrez:
22
3
2023
Statut:
epublish
Résumé
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.
Types de publication
Preprint
Langues
eng
Déclaration de conflit d'intérêts
COMPETING INTERESTS The authors declare the following competing interests: F.B. is an employee of Siemens AG (Munich, Germany). B.v.G. is a shareholder of Thirona (Nijmegen, NL). B.G. is an employee of HeartFlow Inc (California, USA) and Kheiron Medical Technologies Ltd (London, UK). M.M.H. received an Nvidia GPU Grant. Th. K. is an employee of Lunit (Seoul, South Korea). G.L. is on the advisory board of Canon Healthcare IT (Minnetonka, USA) and is a shareholder of Aiosyn BV (Nijmegen, NL). Na.R. is the founder and CSO of Histofy (New York, USA). Ni.R. is an employee of Nvidia GmbH (Munich, Germany). J.S.-R. reports funding from GSK (Heidelberg, Germany), Pfizer (New York, USA) and Sanofi (Paris, France) and fees from Travere Therapeutics (California, USA), Stadapharm (Bad Vilbel, Germany), Astex Therapeutics (Cambridge, UK), Pfizer (New York, USA), and Grunenthal (Aachen, Germany). R.M.S. receives patent royalties from iCAD (New Hampshire, USA), ScanMed (Nebraska, USA), Philips (Amsterdam, NL), Translation Holdings (Alabama, USA) and PingAn (Shenzhen, China); his lab received research support from PingAn through a Cooperative Research and Development Agreement. S.A.T. receives financial support from Canon Medical Research Europe (Edinburgh, Scotland).