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Miguel Ángel González Ballester

Researcher at Pompeu Fabra University

Publications -  218
Citations -  4320

Miguel Ángel González Ballester is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 25, co-authored 194 publications receiving 2913 citations. Previous affiliations of Miguel Ángel González Ballester include T-Systems & Catalan Institution for Research and Advanced Studies.

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Book ChapterDOI

A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis

TL;DR: In this article, the authors proposed an approach based on a Convolutional Neural Network minimizing a hierarchical error function that takes into account not only the finding category, but also its location within the GI tract (lower/upper tract), and the type of finding (pathological finding/therapeutic intervention/anatomical landmark/mucosal views' quality).
Posted ContentDOI

The PNt-Methodology: a top-down network modelling approach to estimate dose- and time-dependent cell responses to complex multifactorial environments

TL;DR: The PNt-Methodology provides a one-of-a-kind network modeling approach to approximate complex multicellular systems and allows for the first time to obtain qualitatively validated cell responses for daily human moving activities and exposure to microgravity.
Book ChapterDOI

An Integrated Approach for Reconstructing a Surface Model of the Proximal Femur from Sparse Input Data and a Multi-Level Point Distribution Model

TL;DR: An integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific surface model of the proximal femur from intra-operatively available sparse data, which may consist of sparse point data or a limited number of calibrated fluoroscopic images.
Book ChapterDOI

Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model.

TL;DR: Results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images, which suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases.
Journal ArticleDOI

Right Ventricular Global and Regional Remodeling in American-Style Football Athletes: A Longitudinal 3D Echocardiographic Study

TL;DR: ASF inter-seasonal training was associated with a proportionate biventricular enlargement, regardless of the field position, and regional RV analysis allowed us to quantify the amount of exercise-induced remodeling that was larger in the apical and inlet regions.