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 https://www.veronikach.com
References
- Source Matters: Source Dataset Impact on Model Robustness in Medical ImagingarXiv preprint arXiv:2403.04484, 2024
- Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardshiparXiv preprint arXiv:2402.06353, 2024
- Revisiting Hidden Representations in Transfer Learning for Medical ImagingTransactions on Machine Learning Research, 2023
- Augmenting Chest X-ray Datasets with Non-Expert AnnotationsarXiv preprint arXiv:2309.02244, 2023
- Machine learning for medical imaging: methodological failures and recommendations for the futureNPJ Digital Medicine, 2022
- Detection of Furigana Text in ImagesarXiv preprint arXiv:2207.03960, 2022
- Predicting Bearings’ Degradation Stages for Predictive Maintenance in the Pharmaceutical IndustryarXiv preprint arXiv:2203.03259, 2022
- Detecting Shortcuts in Medical Images – A Case Study in Chest X-rays2022
- ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classificationarXiv preprint arXiv:2107.12734, 2021
- Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classificationarXiv preprint arXiv:2107.05940, 2021
- High-level prior-based loss functions for medical image segmentation: A surveyComputer Vision and Image Understanding, 2021
- Crowdsourcing airway annotations in chest computed tomography imagesPLoS ONE, 2021
- Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer LearningarXiv preprint arXiv:2006.16633, 2020
- Predicting Scores of Medical Imaging Segmentation Methods with Meta-LearningIn Interpretable and Annotation-Efficient Learning for Medical Image Computing (MICCAI LABELS) , 2020
- Risk of Training Diagnostic Algorithms on Data with Demographic BiasIn Interpretable and Annotation-Efficient Learning for Medical Image Computing (MICCAI LABELS) , 2020
- A survey of crowdsourcing in medical image analysisHuman Computation Journal, 2019
- Cats or CAT scans: Transfer learning from natural or medical image source data sets?Current Opinion in Biomedical Engineering, 2019
- Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysisMedical Image Analysis, 2019
- Automatic Emphysema Detection using Weakly Labeled HRCT Lung ImagesPLoS ONE, 2018
- Crowd disagreement about medical images is informativeIn Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS) , 2018
- Multiple Instance Learning: A Survey of Problem Characteristics and ApplicationsPattern Recognition, 2018
- Transfer learning for multi-center classification of chronic obstructive pulmonary diseaseIEEE Journal of Biomedical and Health Informatics, 2018
- Exploring the similarity of medical imaging classification problemsIn Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS) , 2017
- Dissimilarity-based ensembles for multiple instance learningIEEE Transactions on Neural Networks and Learning Systems, 2016
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- Label stability in multiple instance learningIn Medical Imaging Computing and Computer Assisted Intervention (MICCAI) , 2015
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