Automatic Annotation Tool to Support Supervised Machine Learning for Scaphoid Fracture Detection

Article Article scientifique Article Automatic Annotation Tool to Support Supervised Machine Learning for Scaphoid Fracture Detection eng 2018 author Foufi Vasiliki 848485 author Lanteri Sébastien author Gaudet-Blavignac Christophe 0000-0001-6527-5898 645533 author Remy Pascal 649974 author Montet Xavier Cédric Rodolphe 58743 author Lovis Christian 0000-0002-2681-8076 29016 210-214 10.3233/978-1-61499-921-8-210 30306938 0926-9630 http://ebooks.iospress.nl/publication/50504 The aim of this work is to develop and validate an automatic annotation tool for the detection and bone localization of scaphoid fractures in radiology reports. To achieve this goal, a rule-based method using a Natural Language Processing (NLP) tool was applied. Finite state automata were constructed to detect, classify and annotate reports. An evaluation of the method on a manually annotated dataset has shown 96,8% of total match. Fractures Bone/diagnosis Humans Natural Language Processing Research Report Scaphoid Bone/injuries Supervised Machine Learning Dewey 616.0757 62 Département de radiologie et informatique médicale 227 false 344 Interfaces Homme-machine en milieu clinique 610 Studies in Health Technology and Informatics 255 d022dd65-3c7d-4d0e-a741-b9de3666884f Article (Published version) Public SHTI255-0210.pdf 216661 application/pdf CC BY-NC openAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

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Automatic Annotation Tool to Support Supervised Machine Learning for Scaphoid Fracture Detection	Studies in Health Technology and Informatics			2018	373	64

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Remy, Pascal