A. Kazi, S. Albarqouni, A. Sanchez, C. Kirchhoff, P. Biberthaler, N. Navab, D. Mateus Automatic Classification of Proximal Femur Fractures based on Attention Models Proceedings of MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), Quebec, Canada, September 2017 (bib)
	We target the automatic classication of fractures from clinical X-Ray images following the Arbeitsgemeinschaft Osteosynthese (AO) classication standard. We decompose the problem into the localisation of the region-of-interest (ROI) and the classication of the localized region. Our solution relies on current advances in multi-task end-to-end deep learning. More specially, we adapt an attention model known as Spatial Transformer to learn an image-dependent localization of the ROI trained only from image classication labels. As a case study, we focus here on the classication 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.
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