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Title:

Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

Document type:
Article; Journal Article
Author(s):
Jiménez-Sánchez, Amelia; Mateus, Diana; Kirchhoff, Sonja; Kirchhoff, Chlodwig; Biberthaler, Peter; Navab, Nassir; González Ballester, Miguel A; Piella, Gemma
Abstract:
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the images into types and subtypes according to the fracture's location and complexity. In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN). As...     »
Journal title abbreviation:
Med Image Anal
Year:
2022
Journal volume:
75
Fulltext / DOI:
doi:10.1016/j.media.2021.102273
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34731773
Print-ISSN:
1361-8415
TUM Institution:
595; Klinik und Poliklinik für Unfallchirurgie
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