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Veronika Cheplygina
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Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
V Cheplygina, M De Bruijne, JPW Pluim
Medical image analysis 54, 280-296, 2019
9202019
Multiple instance learning: A survey of problem characteristics and applications
MA Carbonneau, V Cheplygina, E Granger, G Gagnon
Pattern Recognition 77, 329-353, 2018
7332018
Machine learning for medical imaging: methodological failures and recommendations for the future
G Varoquaux, V Cheplygina
NPJ digital medicine 5 (1), 48, 2022
4042022
Metrics reloaded: recommendations for image analysis validation
L Maier-Hein, A Reinke, P Godau, MD Tizabi, F Buettner, E Christodoulou, ...
Nature methods 21 (2), 195-212, 2024
226*2024
Common limitations of image processing metrics: A picture story
A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ...
arXiv preprint arXiv:2104.05642, 2021
1762021
Multiple instance learning with bag dissimilarities
V Cheplygina, DMJ Tax, M Loog
Pattern recognition 48 (1), 264-275, 2015
1572015
High-level prior-based loss functions for medical image segmentation: A survey
R El Jurdi, C Petitjean, P Honeine, V Cheplygina, F Abdallah
Computer Vision and Image Understanding 210, 103248, 2021
992021
Transfer learning for multi-center classification of chronic obstructive pulmonary disease
V Cheplygina, IP Pena, JH Pedersen, DA Lynch, L Sørensen, ...
Journal of Biomedical and Health Informatics 22 (5), 1486 - 1496, 2018
912018
A survey of crowdsourcing in medical image analysis
S Ørting, A Doyle, A van Hilten, M Hirth, O Inel, CR Madan, P Mavridis, ...
arXiv preprint arXiv:1902.09159, 2019
802019
Ten simple rules for getting started on Twitter as a scientist
V Cheplygina, F Hermans, C Albers, N Bielczyk, I Smeets
PLoS Computational Biology 16 (2), e1007513, 2020
782020
Cats or CAT scans: Transfer learning from natural or medical image source data sets?
V Cheplygina
Current Opinion in Biomedical Engineering 9, 21-27, 2019
712019
Understanding metric-related pitfalls in image analysis validation
A Reinke, MD Tizabi, M Baumgartner, M Eisenmann, D Heckmann-Nötzel, ...
Nature methods 21 (2), 182-194, 2024
652024
Dissimilarity-based Ensembles for Multiple Instance Learning
V Cheplygina, DMJ Tax, M Loog
Transactions on Neural Networks and Learning Systems 27 (6), 1379 - 1391, 2016
612016
Single-vs. multiple-instance classification
E Alpaydın, V Cheplygina, M Loog, DMJ Tax
Pattern recognition 48 (9), 2831-2838, 2015
592015
Classification of COPD with Multiple Instance Learning
V Cheplygina, L Sørensen, D Tax, JH Pedersen, M Loog, M de Bruijne
International Conference on Pattern Recognition, 1508-1513, 2014
552014
Risk of training diagnostic algorithms on data with demographic bias
S Abbasi-Sureshjani, R Raumanns, BEJ Michels, G Schouten, ...
Interpretable and Annotation-Efficient Learning for Medical Image Computing …, 2020
522020
On classification with bags, groups and sets
V Cheplygina, DMJ Tax, M Loog
Pattern recognition letters 59, 11-17, 2015
462015
Early experiences with crowdsourcing airway annotations in chest CT
V Cheplygina, A Perez-Rovira, W Kuo, HAWM Tiddens, M De Bruijne
Deep Learning and Data Labeling for Medical Applications: First …, 2016
372016
Bag dissimilarities for multiple instance learning
DMJ Tax, M Loog, RPW Duin, V Cheplygina, WJ Lee
Similarity-Based Pattern Recognition: First International Workshop, SIMBAD …, 2011
372011
Pruned random subspace method for one-class classifiers
V Cheplygina, DMJ Tax
Multiple Classifier Systems: 10th International Workshop, MCS 2011, Naples …, 2011
372011
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