An overview of our publications. The links if available are to the official/published version, but you should always be able to find a preprint on arXiV as well.
2024
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arXiv preprint arXiv:2403.04484, 2024
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arXiv preprint arXiv:2402.03003, 2024
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2023
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Transactions on Machine Learning Research, 2023
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arXiv preprint arXiv:2309.02244, 2023
2022
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NPJ Digital Medicine, 2022
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arXiv preprint arXiv:2207.03960, 2022
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arXiv preprint arXiv:2203.03259, 2022
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Computer Methods and Programs in Biomedicine, 2022
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2021
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MELBA (Machine Learning for Biomedical Imaging), 2021
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arXiv preprint arXiv:2107.05940, 2021
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Computer Vision and Image Understanding, 2021
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2020
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arXiv preprint arXiv:2006.16633, 2020
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In Interpretable and Annotation-Efficient Learning for Medical Image Computing (MICCAI LABELS) , 2020
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In Interpretable and Annotation-Efficient Learning for Medical Image Computing (MICCAI LABELS) , 2020
2019
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Silas Ørting , Andrew Doyle , Arno Hilten , Matthias Hirth , Oana Inel , and 4 more authors
Human Computation Journal, 2019
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Current Opinion in Biomedical Engineering, 2019
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Medical Image Analysis, 2019
2018
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Isabel Pino Pena ,
Veronika Cheplygina, Sofia Paschaloudi , Morten Vuust , Jesper Carl , and
3 more authors PLoS ONE, 2018
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In Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS) , 2018
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Pattern Recognition, 2018
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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
2017
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In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI LABELS) , 2017
2016
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IEEE Transactions on Neural Networks and Learning Systems, 2016
2015
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In Similarity-Based Pattern Recognition , 2015
In many pattern recognition problems, a single feature vector is not sufficient to describe an object. In multiple instance learning (MIL), objects are represented by sets (bags) of feature vectors (instances). This requires an adaptation of standard supervised classifiers in order to train and evaluate on these bags of instances. Like for supervised classification, several benchmark datasets and numerous classifiers are available for MIL. When performing a comparison of different MIL classifiers, it is important to understand the differences of the datasets, used in the comparison. Seemingly different (based on factors such as dimensionality) datasets may elicit very similar behaviour in classifiers, and vice versa. This has implications for what kind of conclusions may be drawn from the comparison results. We aim to give an overview of the variability of available benchmark datasets and some popular MIL classifiers. We use a dataset dissimilarity measure, based on the differences between the ROC-curves obtained by different classifiers, and embed this dataset dissimilarity matrix into a low-dimensional space. Our results show that conceptually similar datasets can behave very differently. We therefore recommend examining such dataset characteristics when making comparisons between existing and new MIL classifiers. Data and other resources are available at http://www.miproblems.org.
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Pattern Recognition Letters, 2015
Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas.
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V. Cheplygina, L. Sørensen , D. M. J. Tax , M. Bruijne , and M. Loog
In Medical Imaging Computing and Computer Assisted Intervention (MICCAI) , 2015
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Pattern Recognition, 2015
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, but do not generalize to the whole range of MIL problems. Other MIL methods shift the focus of assumptions from the labels to the overall (dis)similarity of bags, and therefore learn from bags directly. We propose to represent each bag by a vector of its dissimilarities to other bags in the training set, and treat these dissimilarities as a feature representation. We show several alternatives to define a dissimilarity between bags and discuss which definitions are more suitable for particular MIL problems. The experimental results show that the proposed approach is computationally inexpensive, yet very competitive with state-of-the-art algorithms on a wide range of MIL datasets.