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 | 882 | 2019 |
Multiple instance learning: A survey of problem characteristics and applications MA Carbonneau, V Cheplygina, E Granger, G Gagnon Pattern Recognition 77, 329-353, 2018 | 697 | 2018 |
Machine learning for medical imaging: methodological failures and recommendations for the future G Varoquaux, V Cheplygina NPJ digital medicine 5 (1), 48, 2022 | 344 | 2022 |
Metrics reloaded: Pitfalls and recommendations for image analysis validation L Maier-Hein, B Menze arXiv. org, 2022 | 170* | 2022 |
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 | 158 | 2021 |
Multiple instance learning with bag dissimilarities V Cheplygina, DMJ Tax, M Loog Pattern recognition 48 (1), 264-275, 2015 | 153 | 2015 |
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 | 90 | 2021 |
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 | 84 | 2018 |
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 | 78 | 2019 |
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 | 74 | 2020 |
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 | 65 | 2019 |
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 | 61 | 2016 |
Single-vs. multiple-instance classification E Alpaydın, V Cheplygina, M Loog, DMJ Tax Pattern recognition 48 (9), 2831-2838, 2015 | 59 | 2015 |
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 | 54 | 2014 |
On classification with bags, groups and sets V Cheplygina, DMJ Tax, M Loog Pattern recognition letters 59, 11-17, 2015 | 45 | 2015 |
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 | 44 | 2020 |
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 | 38 | 2024 |
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 | 37 | 2011 |
Pruned random subspace method for one-class classifiers V Cheplygina, DMJ Tax Multiple Classifier Systems: 10th International Workshop, MCS 2011, Naples …, 2011 | 37 | 2011 |
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 | 36 | 2016 |