Veronika Cheplygina

Veronika Cheplygina is an Associate Professor at the IT University of Copenhagen. Her background is in machine learning in general, and based on medical images in particular. She is also thinking about how we do research, and addressing the inefficiencies/inequalities involved. Before ITU, she was faculty member at the Eindhoven University of Technology. Find more info on


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    Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
    Dovile JuodelyteYucheng LuAmelia Jiménez-Sánchez, Sabrina Bottazzi , Enzo Ferrante , and 1 more author
    arXiv preprint arXiv:2403.04484, 2024
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    [Citation needed] Data usage and citation practices in medical imaging conferences
    Théo Sourget, Ahmet Akkoç , Stinna Winther , Christine Lyngbye Galsgaard , Amelia Jiménez-Sánchez, and 3 more authors
    arXiv preprint arXiv:2402.03003, 2024
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    Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
    Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona , Dovile JuodelyteThéo Sourget, Caroline Vang-Larsen , and 2 more authors
    arXiv preprint arXiv:2402.06353, 2024
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    Revisiting Hidden Representations in Transfer Learning for Medical Imaging
    Transactions on Machine Learning Research, 2023
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    Augmenting Chest X-ray Datasets with Non-Expert Annotations
    Cathrine Damgaard , Trine Naja Eriksen , Dovile JuodelyteVeronika Cheplygina, and Amelia Jiménez-Sánchez
    arXiv preprint arXiv:2309.02244, 2023
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    Machine learning for medical imaging: methodological failures and recommendations for the future
    Gaël Varoquaux , and Veronika Cheplygina
    NPJ Digital Medicine, 2022
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    Detection of Furigana Text in Images
    Nikolaj Kjøller Bjerregaard , Veronika Cheplygina, and Stefan Heinrich
    arXiv preprint arXiv:2207.03960, 2022
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    Predicting Bearings’ Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry
    Dovile JuodelyteVeronika Cheplygina, Therese Graversen , and Philippe Bonnet
    arXiv preprint arXiv:2203.03259, 2022
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    Detecting Shortcuts in Medical Images – A Case Study in Chest X-rays
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    ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
    Ralf Raumanns, Gerard Schouten , Max Joosten , Josien PW Pluim , and Veronika Cheplygina
    arXiv preprint arXiv:2107.12734, 2021
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    Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification
    Irma van den Brandt , Floris Fok , Bas Mulders , Joaquin Vanschoren , and Veronika Cheplygina
    arXiv preprint arXiv:2107.05940, 2021
  12. High-level prior-based loss functions for medical image segmentation: A survey
    Rosana El Jurdi , Caroline Petitjean , Paul Honeine , Veronika Cheplygina, and Fahed Abdallah
    Computer Vision and Image Understanding, 2021
  13. Crowdsourcing airway annotations in chest computed tomography images
    Veronika Cheplygina, Adria Perez-Rovira , Wieying Kuo , Harm AWM Tiddens , and Marleen Bruijne
    PLoS ONE, 2021
  14. Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
    Linde S Hesse , Pim A Jong , Josien PW Pluim , and Veronika Cheplygina
    arXiv preprint arXiv:2006.16633, 2020
  15. Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning
    Tom Sonsbeek , and Veronika Cheplygina
    In Interpretable and Annotation-Efficient Learning for Medical Image Computing (MICCAI LABELS) , 2020
  16. Risk of Training Diagnostic Algorithms on Data with Demographic Bias
    Samaneh Abbasi-Sureshjani , Ralf Raumanns, Britt EJ Michels , Gerard Schouten , and Veronika Cheplygina
    In Interpretable and Annotation-Efficient Learning for Medical Image Computing (MICCAI LABELS) , 2020
  17. A survey of crowdsourcing in medical image analysis
    Silas Ørting , Andrew Doyle , Arno Hilten , Matthias Hirth , Oana Inel , and 4 more authors
    Human Computation Journal, 2019
  18. Cats or CAT scans: Transfer learning from natural or medical image source data sets?
    Current Opinion in Biomedical Engineering, 2019
  19. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
    Veronika Cheplygina, Marleen Bruijne , and Josien PW Pluim
    Medical Image Analysis, 2019
  20. Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
    Isabel Pino Pena , Veronika Cheplygina, Sofia Paschaloudi , Morten Vuust , Jesper Carl , and 3 more authors
    PLoS ONE, 2018
  21. Crowd disagreement about medical images is informative
    Veronika Cheplygina, and Josien P W Pluim
    In Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS) , 2018
  22. Multiple Instance Learning: A Survey of Problem Characteristics and Applications
    M.-A. Carbonneau , V. Cheplygina, E. Granger , and G. Gagnon
    Pattern Recognition, 2018
  23. Transfer learning for multi-center classification of chronic obstructive pulmonary disease
    Veronika Cheplygina, Isabel Pino Peña , Jesper Holst Pedersen , David A Lynch , Lauge Sørensen , and 1 more author
    IEEE Journal of Biomedical and Health Informatics, 2018
  24. Exploring the similarity of medical imaging classification problems
    Veronika Cheplygina, Pim Moeskops , Mitko Veta , Behdad Dasht Bozorg , and Josien Pluim
    In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS) , 2017
  25. Dissimilarity-based ensembles for multiple instance learning
    Veronika Cheplygina, David M. J. Tax , and Marco Loog
    IEEE Transactions on Neural Networks and Learning Systems, 2016
  26. Characterizing Multiple Instance Datasets
    Veronika Cheplygina, and David M J Tax
    In Similarity-Based Pattern Recognition , 2015
  27. On classification with bags, groups and sets
    Veronika Cheplygina, David M J Tax , and Marco Loog
    Pattern Recognition Letters, 2015
  28. Label stability in multiple instance learning
    V. Cheplygina, L. Sørensen , D. M. J. Tax , M. Bruijne , and M. Loog
    In Medical Imaging Computing and Computer Assisted Intervention (MICCAI) , 2015
  29. Multiple instance learning with bag dissimilarities
    Veronika Cheplygina, David M. J. Tax , and Marco Loog
    Pattern Recognition, 2015