@inproceedings{a1be4f59a8bb42498622eb3508db6947,
title = "Automatic classification of proximal femur fractures based on attention models",
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.",
author = "Anees Kazi and Shadi Albarqouni and Sanchez, {Amelia Jimenez} and Sonja Kirchhoff and Peter Biberthaler and Nassir Navab and Diana Mateus",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 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 ; Conference date: 10-09-2017 Through 10-09-2017",
year = "2017",
doi = "10.1007/978-3-319-67389-9_9",
language = "English",
isbn = "9783319673882",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "70--78",
editor = "Yinghuan Shi and Heung-Il Suk and Kenji Suzuki and Qian Wang",
booktitle = "Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",
}