G
Gemma Piella
Researcher at Pompeu Fabra University
Publications - 158
Citations - 5510
Gemma Piella 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 143 publications receiving 4411 citations. Previous affiliations of Gemma Piella include Autonomous University of Barcelona & Polytechnic University of Catalonia.
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Journal ArticleDOI
Characterization of myocardial motion patterns by unsupervised multiple kernel learning
Sergio Sanchez-Martinez,Nicolas Duchateau,Tamas Erdei,Alan G. Fraser,Bart Bijnens,Gemma Piella +5 more
TL;DR: The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.
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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.
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Building nonredundant adaptive wavelets by update lifting
TL;DR: A technique for building adaptive wavelets by means of an extension of the lifting scheme that comprises an adaptive update lifting step and a fixed prediction lifting step that can choose between two different update filters.
Adaptive wavelets and their applications to image fusion and compression
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
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A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease.
TL;DR: Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis in Alzheimer's disease.