You can a more-often-updated list of our publications as well as various preprints on Google Scholar.
2022
Varoquaux, G. & Cheplygina, V. (2022) Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digital Medicine 5 (1), 1-8 . Publisher (open access | Github
2021
Raumanns, R., Schouten, G., Joosten, M., Pluim, J.P.W., & Cheplygina, V. (2021). ENHANCE (ENrichting Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification. MELBA December 2021. Publisher (open access) | Github | *Readers award*
Cheplygina, V., Perez-Rovira, A., Kuo, W., Tiddens, H. A. W. M., & de Bruijne, M. (2021) Crowdsourcing Airway Annotations in Chest Computed Tomography Images. PLoS ONE 16(4): e0249580. Publisher | arXiv | Github
El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V. & Abdallah, F. (2021) A surprisingly effective perimeter-based loss for medical image segmentation. Medical Imaging with Deep Learning (MIDL) 2021.
2020
| Cheplygina, V. (2020). CrowdDetective: Wisdom of the Crowds for Detecting Abnormalities in Medical Scans – Rejected Veni 2018 application. Journal of Trial and Error. Publisher | |
| Abbasi-Sureshjani, S., Raumanns, R., Michels, B. E. J., Schouten, G., & Cheplygina, V. (2020). Risk of Training Diagnostic Algorithms on Data with Demographic Bias. MICCAI LABELS 2020. arXiV | |
| van Sonsbeek, T., & Cheplygina, V. (2020). Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning. MICCAI LABELS 2020. arXiV | Github | |
| Ørting, S., Doyle, A., van Hilten, A., Hirth, M. , Inel, O., Madan, C. R., Mavridis, P., Spiers, H. & Cheplygina, V. (2019). A survey of crowdsourcing in medical image analysis. Accepted in Human Computation Journal. arXiV | Data | |
| Cheplygina, V., Hermans, F., Albers, C., Bielczyk, N., & Smeets, I. (2020). Ten simple rules for getting started on Twitter as a scientist. PLoS Computational Biology 16(2): e1007513. Publisher (Open access) | |
| Bielczyk, N., et. al. (2020). Effective Self-Management for Early Career Researchers in the Natural and Life Sciences. Neuron, 106(2), 212-217. Publisher |
2019
| Cheplygina, Veronika, Marleen de Bruijne, and Josien P W Pluim. “Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.” Medical Image Analysis, in press. arXiV | Publisher | Data | |
| Cheplygina, Veronika. “Cats or CAT scans: transfer learning from natural or medical image source datasets?”. Current Opinion in Biomedical Engineering, in press, 2019. arXiv | Publisher |
2018
| Peña, Isabel Pino, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, and Marleen de Bruijne. “Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images.” PLoS ONE, 13(10): e0205397, 2018. arXiV | Publisher | Data | Code | |
| Cheplygina, Veronika and Josien P W Pluim. “Crowd disagreement about medical images is informative”. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS), pp. 105-111. Springer, 2018. arXiv | Publisher | Data | |
| Ørting, Silas, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M W Wille, and Marleen de Bruijne. Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS), pp. 140-149. Springer, 2018. arXiV | |
| Carbonneau, Marc-André, Veronika Cheplygina, Eric Granger, and Ghyslain Gagnon. “Multiple instance learning: A survey of problem characteristics and applications.” Pattern Recognition (2018). arXiV | Publisher | Code |
2017
| Cheplygina, Veronika, Pim Moeskops, Mitko Veta, Behdad Dashtbozorg, and Josien P W Pluim. “Exploring the similarity of medical imaging classification problems.” In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS), pp. 59-66. Springer, 2017. arXiV | Publisher | |
| Ørting, Silas Nyboe, Veronika Cheplygina, Jens Petersen, Laura H. Thomsen, Mathilde M W Wille, and Marleen de Bruijne. “Crowdsourced emphysema assessment.” In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS), pp. 126-135. Springer, 2017. PDF | Publisher | |
| Cheplygina, Veronika, Isabel Pino Pena, Jesper Holst Pedersen, David A. Lynch, Lauge Sorensen, and Marleen de Bruijne. “Transfer learning for multi-center classification of chronic obstructive pulmonary disease.” IEEE Journal of Biomedical and Health Informatics (2017). arXiV | Publisher |
2016
| Cheplygina, Veronika, David MJ Tax, and Marco Loog. “Dissimilarity-based ensembles for multiple instance learning.” IEEE transactions on neural networks and learning systems 27, no. 6 (2016): 1379-1391. arXiV | Publisher | MATLAB Code | |
| Cheplygina, Veronika, Adria Perez-Rovira, Wieying Kuo, Harm AWM Tiddens, and Marleen de Bruijne. “Early experiences with crowdsourcing airway annotations in chest CT.” In Deep Learning and Data Labeling for Medical Applications, pp. 209-218. Springer, 2016. arXiV | Publisher | Poster | |
| Tax, David MJ, Veronika Cheplygina, Robert PW Duin, and Jan van de Poll. “The Similarity Between Dissimilarities.” In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp. 84-94. Springer, 2016. PDF | Publisher | |
| Cheplygina, Veronika, Annegreet van Opbroek, M. Arfan Ikram, Meike W. Vernooij, and Marleen de Bruijne. “Asymmetric similarity-weighted ensembles for image segmentation.” In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pp. 273-277. IEEE, 2016. PDF | Publisher | Blog Post |
2015
| Cheplygina, Veronika. “Dissimilarity-Based Multiple Instance Learning.” PhD thesis, Delft University of Technology (2015). PDF | Cover | Online | |
| Cheplygina, Veronika, Lauge Sørensen, David MJ Tax, Marleen de Bruijne, and Marco Loog. “Label stability in multiple instance learning.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 539-546. Springer, 2015. arXiV | Publisher | Poster | Video | Blog Post | | |
| Cheplygina, Veronika, and David MJ Tax. “Characterizing multiple instance datasets.” In International Workshop on Similarity-Based Pattern Recognition, pp. 15-27. Springer, 2015. PDF | Publisher | Poster | Slides | |
| Cheplygina, Veronika, David MJ Tax, and Marco Loog. “On classification with bags, groups and sets.” Pattern Recognition Letters 59 (2015): 11-17. arXiV | Publisher | |
| Alpaydın, Ethem, Veronika Cheplygina, Marco Loog, and David MJ Tax. “Single-vs. multiple-instance classification.” Pattern Recognition 48, no. 9 (2015): 2831-2838. PDF | Publisher | Code | Data | |
| Cheplygina, Veronika, David MJ Tax, and Marco Loog. “Multiple instance learning with bag dissimilarities.” Pattern Recognition 48, no. 1 (2015): 264-275. arXiV | Publisher | Code | Data |
2014
Cheplygina, Veronika, David MJ Tax, Marco Loog, and Aasa Feragen. “Network-guided group feature selection for classification of autism spectrum disorder.” In International Workshop on Machine Learning in Medical Imaging, pp. 190-197. Springer, 2014. PDF | Publisher
Cheplygina, Veronika, Lauge Sørensen, David MJ Tax, Jesper Holst Pedersen, Marco Loog, and Marleen de Bruijne. “Classification of COPD with multiple instance learning.” In Pattern Recognition (ICPR), 2014 22nd International Conference on, pp. 1508-1513. IEEE, 2014. arXiV | Publisher
2013
Plasencia-Calaña, Yenisel, Veronika Cheplygina, Robert PW Duin, Edel B. García-Reyes, Mauricio Orozco-Alzate, David MJ Tax, and Marco Loog. “On the informativeness of asymmetric dissimilarities.” In International Workshop on Similarity-Based Pattern Recognition, pp. 75-89. Springer, Berlin, Heidelberg, 2013. PDF
Cheplygina, Veronika, David MJ Tax, and Marco Loog. “Combining instance information to classify bags.” In International Workshop on Multiple Classifier Systems, pp. 13-24. Springer, Berlin, Heidelberg, 2013. PDF | Publisher
2012
Cheplygina, Veronika, David MJ Tax, and Marco Loog. “Does one rotten apple spoil the whole barrel?.” In Pattern Recognition (ICPR), 2012 21st International Conference on, pp. 1156-1159. IEEE, 2012. PDF | Publisher
Lee, Wan-Jui, Veronika Cheplygina, David MJ Tax, Marco Loog, and Robert PW Duin. “Bridging structure and feature representations in graph matching.” International Journal of Pattern Recognition and Artificial Intelligence 26, no. 05 (2012): 1260005. PDF
Cheplygina, Veronika, David MJ Tax, and Marco Loog. “Class-dependent dissimilarity measures for multiple instance learning.” In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp. 602-610. Springer, Berlin, Heidelberg, 2012. PDF
2011
Cheplygina, Veronika, and David MJ Tax. “Pruned random subspace method for one-class classifiers.” In International Workshop on Multiple Classifier Systems, pp. 96-105. Springer, Berlin, Heidelberg, 2011. PDF | Publisher
Tax, David MJ, Marco Loog, Robert PW Duin, Veronika Cheplygina, and Wan-Jui Lee. “Bag dissimilarities for multiple instance learning.” In International Workshop on Similarity-Based Pattern Recognition, pp. 222-234. Springer, Berlin, Heidelberg, 2011. PDF | Publisher
2010
Cheplygina, Veronika, and David MJ Tax. “Random subspace method for one-class classifiers.” Master’s thesis, Delft University of Technology, 2010. PDF