V
Vicent J. Ribas
Publications - 9
Citations - 80
Vicent J. Ribas is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 7 publications receiving 34 citations.
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Journal ArticleDOI
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline
Ilaria Bonavita,Xavier Rafael-Palou,Mario Ceresa,Gemma Piella,Vicent J. Ribas,Miguel Ángel González Ballester +5 more
TL;DR: This article proposes to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection by integrating predictive models of nodulemalignancy into a limited size lung cancer datasets.
Journal ArticleDOI
Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks
Xavier Rafael-Palou,Anton Aubanell,Ilaria Bonavita,Mario Ceresa,Gemma Piella,Vicent J. Ribas,Miguel Ángel González Ballester +6 more
TL;DR: A novel method based on a 3D siamese neural network is presented, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration.
Posted Content
Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks
Xavier Rafael-Palou,Anton Aubanell,Ilaria Bonavita,Mario Ceresa,Gemma Piella,Vicent J. Ribas,Miguel Ángel González Ballester +6 more
TL;DR: In this article, a 3D siamese neural network was used to detect, match, and predict nodule growth given pairs of CT scans of the same patient without the need for image registration.
Journal ArticleDOI
Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network
Xavier Rafael-Palou,Anton Aubanell,Mario Ceresa,Vicent J. Ribas,Gemma Piella,Miguel Ángel González Ballester +5 more
TL;DR: In this article , a deep hierarchical generative and probabilistic network is proposed to predict whether a lung nodule will grow, remain stable or regress over time, especially early in its follow-up, which would help doctors prescribe personalized treatments and better surgical planning.
Posted Content
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,Vicent J. Ribas,Gemma Piella,Miguel Ángel González Ballester +5 more
TL;DR: In this article, a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through detection of growth in the nodules.