Miguel Ángel González Ballester

TL;DR: In this paper, the authors proposed principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction.

TL;DR: In this paper, two pretrained encoder-decoder networks are sequentially stacked: the second network takes as input the concatenation of the original frame and the initial prediction generated by the first network, which acts as an attention mechanism enabling the second networks to focus on interesting areas within the image, thereby improving the quality of its predictions.

TL;DR: This work aims to efficiently segment different intrauterine tissues in fetal magnetic resonance imaging (MRI) and 3D ultrasound and suggests that combining the selected 10 radiomic features per anatomy along with DeepLabV3+ or BiSeNet architectures for MRI, and PSPNet or Tiramisu for 3D US, can lead to the highest fetal / maternal tissue segmentation performance, robustness, informativeness, and heterogeneity.

TL;DR: An automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea that incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model is proposed.

TL;DR: A strategy to couple the discrete biological model at the molecular /cellular level and the biomechanical finite element simulations at the tissue level and found that it can indeed simulate the evolution of clinical image biomarkers during disease progression.