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.
Papers
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Journal ArticleDOI
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
TL;DR: In this article, a survey of semi-supervised, multiple instance and transfer learning in medical image segmentation is presented, and connections between these learning scenarios, and opportunities for future research are discussed.
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Multiple instance learning: A survey of problem characteristics and applications
TL;DR: A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.
Journal ArticleDOI
Multiple instance learning with bag dissimilarities
TL;DR: A general bag dissimilarities framework for multiple instance learning is explored and several alternatives to define a dissimilarity between bags are shown and discussed, which definitions are more suitable for particular MIL problems.
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Machine learning for medical imaging: methodological failures and recommendations for the future
TL;DR: In this article , the authors review roadblocks to developing and assessing methods in computer analysis of medical images and provide recommendations on how to further address these problems in the future, and also discuss on-going efforts to counteract these problems.
BookDOI
Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
M.J. Cardoso,Tal Arbel,Veronika Cheplygina,S.-L. Lee,Simone Balocco,Diana Mateus,Guillaume Zahnd,Lena Maier-Hein,Stefanie Demirci,Eric Granger,Luc Duong,Marc-André Carbonneau,Shadi Albarqouni,Gustavo Carneiro +13 more
TL;DR: A technique to automatically estimate circular cross-sections of the vessels in CT scans by using the Hough transform and a parametric snake model to estimate the local probability density functions of the image intensity inside and outside the vessels.