Automatic classification of proximal femur fractures based on attention models
Kazi A, Albarqouni S, Sanchez AJ, Kirchhoff S, Biberthaler P, Navab N, Mateus D (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10541 LNCS
Pages Range: 70-78
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Quebec City, QC, CAN
ISBN: 9783319673882
DOI: 10.1007/978-3-319-67389-9_9
Abstract
We target the automatic classification of fractures from clinical X-Ray images following the Arbeitsgemeinschaft Osteosynthese (AO) classification standard. We decompose the problem into the localization of the region-of-interest (ROI) and the classification of the localized region. Our solution relies on current advances in multi-task end-to-end deep learning. More specifically, we adapt an attention model known as Spatial Transformer (ST) to learn an image-dependent localization of the ROI trained only from image classification labels. As a case study, we focus here on the classification of proximal femur fractures. We provide a detailed quantitative and qualitative validation on a dataset of 1000 images and report high accuracy with regard to inter-expert correlation values reported in the literature.
Involved external institutions
Germany (DE)How to cite
APA:
Kazi, A., Albarqouni, S., Sanchez, A.J., Kirchhoff, S., Biberthaler, P., Navab, N., & Mateus, D. (2017). Automatic classification of proximal femur fractures based on attention models. In Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 70-78). Quebec City, QC, CAN: Springer Verlag.
MLA:
Kazi, Anees, et al. "Automatic classification of proximal femur fractures based on attention models." Proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang, Springer Verlag, 2017. 70-78.
BibTeX: Download