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Evgenia Papavasileiou
Researcher at Vrije Universiteit Brussel
Publications - 13
Citations - 867
Evgenia Papavasileiou is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Neuroevolution & Feature selection. The author has an hindex of 5, co-authored 12 publications receiving 529 citations. Previous affiliations of Evgenia Papavasileiou include University of Patras & iMinds.
Papers
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Journal ArticleDOI
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.
Arnaud Arindra Adiyoso Setio,Alberto Traverso,Thomas de Bel,Moira S.N. Berens,Cas van den Bogaard,Piergiorgio Cerello,Hao Chen,Qi Dou,Maria Evelina Fantacci,Bram Geurts,Robbert van der Gugten,Pheng-Ann Heng,Bart Jansen,Michael M.J. de Kaste,Valentin Kotov,Jack Yu-Hung Lin,Jeroen Manders,Alexander Sóñora-Mengana,Juan C. García-Naranjo,Evgenia Papavasileiou,Mathias Prokop,M. Saletta,Cornelia M. Schaefer-Prokop,Ernst T. Scholten,Luuk Scholten,Miranda M. Snoeren,Ernesto Lopez Torres,Jef Vandemeulebroucke,Nicole Walasek,Guido Zuidhof,Bram van Ginneken,Colin Jacobs +31 more
TL;DR: The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.
Journal ArticleDOI
A Systematic Literature Review of the Successors of "NeuroEvolution of Augmenting Topologies".
TL;DR: A systematic literature review (SLR) is presented to list and categorize the methods succeeding NEAT and proposes a new categorization scheme of NEAT's successors into three clusters.
Proceedings ArticleDOI
An investigation of topological choices in FS-NEAT and FD-NEAT on XOR-based problems of increased complexity
TL;DR: This paper investigates how the choice of the number of initially connected inputs affects the performance of FD-NEAT and FS- NEAT in terms of accuracy, number of generations required for convergence, ability of performing feature selection and size of the evolved networks.
Posted Content
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Abel Diaz Berenguer,Hichem Sahli,Boris Joukovsky,Maryna Kvasnytsia,Ine Dirks,Mitchel Alioscha-Perez,Nikos Deligiannis,Panagiotis Gonidakis,Sebastián Amador Sánchez,Redona Brahimetaj,Evgenia Papavasileiou,Jonathan Cheung-Wai Chan,Fei Li,Shangzhen Song,Yixin Yang,Sofie Tilborghs,S. Willems,Tom Eelbode,Jeroen Bertels,Dirk Vandermeulen,Frederik Maes,Paul Suetens,Lucas Fidon,Tom Vercauteren,David Robben,Arne Brys,Dirk Smeets,Bart Ilsen,Nico Buls,Nina Watté,Johan De Mey,Annemiek Snoeckx,Paul M. Parizel,Julien Guiot,Louis Deprez,Paul Meunier,Stefaan Gryspeerdt,Kristof De Smet,Bart Jansen,Jef Vandemeulebroucke +39 more
TL;DR: An explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding for COVID-19 classification and envisage a wide deployment of the developed technique in large-scale clinical studies.
Proceedings ArticleDOI
A comparison between FS-NEAT and FD-NEAT and an investigation of different initial topologies for a classification task with irrelevant features
TL;DR: The results show that the choice of the initial topology can affect the performance of the two algorithms, resulting in higher accuracy, faster convergence and better feature selection abilities.