Abstract

The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1%, which is better than the expert radiologists are on average.
OriginalspracheEnglisch
TitelProceedings of the Fourth Conference on Medical Imaging with Deep Learning
Redakteure/-innenMattias Heinrich, Qi Dou, Marleen de Bruijne, Jan Lellmann, Alexander Schläfer, Floris Ernst
Seitenumfang13
Band143
Herausgeber (Verlag)PMLR
Erscheinungsdatum01.07.2021
Seiten484-496
PublikationsstatusVeröffentlicht - 01.07.2021

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

DFG-Fachsystematik

  • 4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing
  • Neuronale Netze

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