Miguel Ángel González Ballester

TL;DR: A framework to fit a 3D Morphable Model representing the breast anatomy of the patient using either 3D scans or 2D photos using a Weighted Regularized projection into the shape space which allows to regularize a given shape towards a prior shape which is not necessarily the statistcal model mean shape.

TL;DR: In this paper, a curriculum learning approach was proposed for the automatic classification of proximal femur fractures from X-ray images, where three curriculum strategies were used: individually weighting training samples, reordering the training set, and sampling subsets of data.

TL;DR: An automatic framework is employed, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation, that has a great potential to help in both surgical planning decisions and in the audiological setting process.

TL;DR: The EarlyCause project as discussed by the authors, a large-scale and inter-disciplinary research project funded by the European Union's Horizon 2020 research and innovation programme, takes advantage of human longitudinal birth cohort data, animal studies and cellular models to test the hypothesis of shared mechanisms and molecular pathways by which early life stress shapes an individual's physical and mental health in adulthood.

TL;DR: The feasibility and accuracy of computer-assisted graft implantation for tissue-engineered replacement of the human ankle joint is demonstrated, however, the present type of registration by pair-point matching with anatomical landmarks cannot be recommended due to the considerable inaccuracies.