S
Svitlana Zinger
Researcher at Eindhoven University of Technology
Publications - 149
Citations - 2141
Svitlana Zinger is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Rendering (computer graphics) & Context (language use). The author has an hindex of 20, co-authored 143 publications receiving 1540 citations. Previous affiliations of Svitlana Zinger include French Alternative Energies and Atomic Energy Commission & Télécom ParisTech.
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Journal ArticleDOI
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge
Péter Bándi,Oscar Geessink,Quirine F. Manson,Marcory C. R. F. van Dijk,Maschenka Balkenhol,Meyke Hermsen,Babak Ehteshami Bejnordi,Byungjae Lee,Kyunghyun Paeng,Aoxiao Zhong,Quanzheng Li,Farhad Ghazvinian Zanjani,Svitlana Zinger,Keisuke Fukuta,Daisuke Komura,Vlado Ovtcharov,Shenghua Cheng,Shaoqun Zeng,Jeppe Thagaard,Anders Bjorholm Dahl,Huangjing Lin,Hao Chen,Ludwig Jacobsson,Martin Hedlund,Melih cetin,Eren Halici,Hunter Jackson,Richard J. Chen,Fabian Both,Jörg Franke,Heidi V.N. Küsters-Vandevelde,Willem Vreuls,Peter Bult,Bram van Ginneken,Jeroen van der Laak,Geert Litjens +35 more
TL;DR: It is shown that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.
Journal ArticleDOI
Computer-aided detection of early neoplastic lesions in Barrett’s esophagus
Fons van der Sommen,Svitlana Zinger,Wouter L. Curvers,Raf Bisschops,Oliver Pech,Bas L. Weusten,Jacques J. Bergman,Erik J. Schoon +7 more
TL;DR: The automated computer algorithm developed in this study was able to identify early neoplastic lesions with reasonable accuracy, suggesting that automated detection ofEarly neoplasia in Barrett's esophagus is feasible.
Proceedings ArticleDOI
Stain normalization of histopathology images using generative adversarial networks
TL;DR: A novel approach to computational histopathology by introducing a parametric, fully unsupervised generative model based on end-to-end machine learning in the framework of generative adversarial networks that outperforms most state-of-the-art methods.
Journal ArticleDOI
Technical Aspects of Neurostimulation: Focus on Equipment, Electric Field Modeling, and Stimulation Protocols
Debby Klooster,de Aja Anton Louw,B Albert Aldenkamp,Rmh René Besseling,Rmc Rob Mestrom,Sofie Carrette,Svitlana Zinger,Jwm Jan Bergmans,W Werner Mess,Kristl Vonck,Evelien Carrette,L Lisanne Breuer,Antoine Bernas,AG Anton Tijhuis,P Paul Boon +14 more
TL;DR: This review provides an overview of the technical basis of neurostimulation focusing on the equipment, the present understanding of induced electric fields, and the stimulation protocols.
Journal ArticleDOI
Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism.
TL;DR: This study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.