109 citations · 283 across the 11 of their papers we have counts for
13 papers
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Valentin Comte, Mireia Alenya, Andrea Urru +10
Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditiona…
An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
Andrea Urru, Ayako Nakaki, Oualid Benkarim +9
The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While…
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs
Araceli Morales, Antonio R. Porras, Marius George Linguraru +2
We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies d…
Memory-aware curriculum federated learning for breast cancer classification
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel A. González Ballester +2
For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative sample…
An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth
Xavier Rafael-Palou, Anton Aubanell, Mario Ceresa +3
Early detection and quantification of tumour growth would help clinicians to prescribe more accurate treatments and provide better surgical planning. However, the multifactorial an…
Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
Xavier Rafael-Palou, Anton Aubanell, Mario Ceresa +3
We address the problem of supporting radiologists in the longitudinal management of lung cancer. Therefore, we proposed a deep learning pipeline, composed of four stages that compl…
Survey on 3D face reconstruction from uncalibrated images
Araceli Morales, Gemma Piella, Federico M. Sukno
Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face,…
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…
Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
Xavier Rafael-Palou, Anton Aubanell, Ilaria Bonavita +4
Nodule malignancy assessment is a complex, time-consuming and error-prone task. Current clinical practice requires measuring changes in size and density of the nodule at different…
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…
Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks
Xavier Rafael-Palou, Anton Aubanell, Ilaria Bonavita +4
Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient ne…
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline
Ilaria Bonavita, Xavier Rafael-Palou, Mario Ceresa +3
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done b…
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation
Santi Puch, Irina Sánchez, Aura Hernández +2
In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the…