1. source.png
    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
  2. citation.png
    [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
  3. actionability.png
    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


  1. revisiting.png
    Revisiting Hidden Representations in Transfer Learning for Medical Imaging
    Transactions on Machine Learning Research, 2023
  2. augmenting.png
    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


  1. kaggle.png
    Machine learning for medical imaging: methodological failures and recommendations for the future
    Gaël Varoquaux , and Veronika Cheplygina
    NPJ Digital Medicine, 2022
  2. furigana.png
    Detection of Furigana Text in Images
    Nikolaj Kjøller Bjerregaard , Veronika Cheplygina, and Stefan Heinrich
    arXiv preprint arXiv:2207.03960, 2022
  3. pharmaceutical.png
    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
  4. fed-curriculum.png
    Memory-aware curriculum federated learning for breast cancer classification
    Amelia Jiménez-Sánchez, Mickael Tardy , Miguel A González Ballester , Diana Mateus , and Gemma Piella
    Computer Methods and Programs in Biomedicine, 2022
  5. shortcuts.png
    Detecting Shortcuts in Medical Images – A Case Study in Chest X-rays


  1. enhance.png
    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
  2. cat-scans.png
    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
  3. 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
  4. Crowdsourcing airway annotations in chest computed tomography images
    Veronika Cheplygina, Adria Perez-Rovira , Wieying Kuo , Harm AWM Tiddens , and Marleen Bruijne
    PLoS ONE, 2021


  1. 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
  2. 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
  3. 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


  1. 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
  2. Cats or CAT scans: Transfer learning from natural or medical image source data sets?
    Current Opinion in Biomedical Engineering, 2019
  3. 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


  1. 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
  2. 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
  3. Multiple Instance Learning: A Survey of Problem Characteristics and Applications
    M.-A. Carbonneau , V. Cheplygina, E. Granger , and G. Gagnon
    Pattern Recognition, 2018
  4. 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


  1. 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


  1. 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


  1. Characterizing Multiple Instance Datasets
    Veronika Cheplygina, and David M J Tax
    In Similarity-Based Pattern Recognition , 2015
  2. On classification with bags, groups and sets
    Veronika Cheplygina, David M J Tax , and Marco Loog
    Pattern Recognition Letters, 2015
  3. 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
  4. Multiple instance learning with bag dissimilarities
    Veronika Cheplygina, David M. J. Tax , and Marco Loog
    Pattern Recognition, 2015