V
Veronika Cheplygina
Researcher at Eindhoven University of Technology
Publications - 80
Citations - 2198
Veronika Cheplygina is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 18, co-authored 68 publications receiving 1314 citations. Previous affiliations of Veronika Cheplygina include Erasmus University Rotterdam & Delft University of Technology.
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Journal ArticleDOI
Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease
Veronika Cheplygina,Isabel Pino Pena,Jesper Holst Pedersen,David A. Lynch,Lauge Sørensen,Marleen de Bruijne +5 more
TL;DR: In this paper, the authors used Gaussian texture features and a weighted logistic classifier for the classification of COPD in a multicenter dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions.
Journal ArticleDOI
Ten simple rules for getting started on Twitter as a scientist
Veronika Cheplygina,Felienne Hermans,Felienne Hermans,Casper J. Albers,Natalia Z. Bielczyk,Ionica Smeets +5 more
TL;DR: Ten simple rules to help researchers who are planning to start their journey on Twitter to take their first steps and advance their careers using Twitter.
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Cats or CAT scans: Transfer learning from natural or medical image source data sets?
TL;DR: A number of research directions the authors need to take as a community to gain more understanding of transfer learning are discussed and the answer to which strategy is best seems to be ‘it depends’.
Journal ArticleDOI
Metrics reloaded: Pitfalls and recommendations for image analysis validation
Lena Maier-Hein,Annika Reinke,Evangelia Christodoulou,Ben Glocker,Patrick Godau,Fabian Isensee,Jens Kleesiek,Michal Kozubek,Mauricio Reyes,Michael Riegler,Manuel Wiesenfarth,Michael Baumgartner,Matthias Eisenmann,Doreen Heckmann-Notzel,A. Emre Kavur,Tim Rädsch,Minu D. Tizabi,Laura Acion,Michela Antonelli,Tal Arbel,Spyridon Bakas,Peter Bankhead,Arriel Benis,M. Jorge Cardoso,Veronika Cheplygina,Beth A. Cimini,Gary S. Collins,Keyvan Farahani,Bram van Ginneken,Daniel A. Hashimoto,Michael M. Hoffman,Merel Huisman,Pierre Jannin,Charles E. Kahn,Alexandros Karargyris,Alan Karthikesalingam,Hannes Kenngott,Annette Kopp-Schneider,Anna Kreshuk,T. Kurca,Bennett A. Landman,Geert Litjens,Amin Madani,Klaus H. Maier-Hein,Anne L. Martel,Peter Mattson,Erik Meijering,Bjoern H. Menze,David Moher,Karel G.M. Moons,H. M. Muller,Felix Nickel,B. Nichyporuk,Jens Petersen,Nasir M. Rajpoot,Nicola Rieke,Julio Saez-Rodriguez,Clarisa S'anchez Guti'errez,Shravya Shetty,Maarten van Smeden,Carole H. Sudre,Ronald M Summers,Abdel Aziz Taha,Sotirios A. Tsaftaris,Ben Van Calster,Gaël Varoquaux,Paul F. Jager +66 more
TL;DR: The Metrics Reloaded framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection from the domain interest to the properties of the target structure(s), data set and algorithm output.
Journal ArticleDOI
Dissimilarity-Based Ensembles for Multiple Instance Learning
TL;DR: A third, intermediate approach is proposed, which links the two approaches and combines their strengths and is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes.