48 citations · 121 across the 4 of their papers we have counts for
4 papers
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
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 sys…
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several st…
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray ima…
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing wi…