PURRlab @ IT University of Copenhagen
Pattern Recognition Revisited
About
PURRLab research interests lie within the broad area of trustworthy machine learning and its applications to medical imaging with a focus on datasets. We are particularly interested in understanding the similarity and diversity of datasets, methods for learning with limited labeled data such as transfer learning, and meta-research on machine learning in medical imaging.
The best way to get a sense of what’s currently going on in the lab is to read about our people and projects.
PURRLab is a part of DASYA research group in the department of Computer Science at the IT University of Copenhagen and is led by Veronika Cheplygina.
PURRLab research is being supported by the Dutch Research Council, Novo Nordisk Foundation and the Independent Research Council of Denmark.
News
Mar 14, 2024 | We have another new preprint to share, this time on model robustness in transfer learning. |
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Feb 15, 2024 | Two new preprints are out, on citation practices and sharing datasets on community platforms, both about medical imaging datasets. |
Jan 31, 2024 | Several of us will be at the D3A conference, presenting posters about shortcuts, robustness in transfer learning, and citations of medical imaging datasets. Please stop by if you are interested in our work! |
Dec 01, 2023 | Yucheng Lu joins us as a postdoctoral researcher, welcome Yucheng! |
Oct 09, 2023 | Théo Sourget now joins us as a research assistant, welcome Théo! |
Sep 29, 2023 | Dovile’s paper was accepted at TMLR! Amelia will be presenting her work with MSc students Trine and Cathrine at the ICCV DataComp workshop in Paris next week. Come say hi! |
Jun 07, 2023 | Presentations at Health Data Science Day |
Selected publications
- 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
- ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classificationarXiv preprint arXiv:2107.12734, 2021