A
Amelia Jiménez-Sánchez
Researcher at Pompeu Fabra University
Publications - 11
Citations - 192
Amelia Jiménez-Sánchez is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: MNIST database & Convolutional neural network. The author has an hindex of 6, co-authored 11 publications receiving 139 citations.
Papers
More filters
Book ChapterDOI
Capsule Networks Against Medical Imaging Data Challenges
TL;DR: The results suggest that capsule networks can be trained with less amount of data for the same or better performance and are more robust to an imbalanced class distribution, which makes the approach very promising for the medical imaging community.
Book ChapterDOI
Capsule Networks against Medical Imaging Data Challenges
TL;DR: In this article, the authors compare capsule networks against ConvNets under typical datasets constraints of medical image analysis, namely, small amounts of annotated data and class-imbalance.
Book ChapterDOI
Medical-based deep curriculum learning for improved fracture classification
Amelia Jiménez-Sánchez,Diana Mateus,Sonja Kirchhoff,Chlodwig Kirchhoff,Peter Biberthaler,Nassir Navab,Miguel Ángel González Ballester,Gemma Piella +7 more
TL;DR: In this paper, the authors proposed and compared several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra-and inter-expert disagreement.
Journal ArticleDOI
Precise proximal femur fracture classification for interactive training and surgical planning
Amelia Jiménez-Sánchez,Anees Kazi,Shadi Albarqouni,Shadi Albarqouni,Chlodwig Kirchhoff,Peter Biberthaler,Nassir Navab,Sonja Kirchhoff,Diana Mateus +8 more
TL;DR: In this article, a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification is presented.
Book ChapterDOI
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Amelia Jiménez-Sánchez,Diana Mateus,Sonja Kirchhoff,Chlodwig Kirchhoff,Peter Biberthaler,Nassir Navab,Miguel Ángel González Ballester,Gemma Piella +7 more
TL;DR: This work proposes and compares several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement.