Amelia Jiménez-Sánchez

Amelia is a Postdoctoral Researcher at the IT University of Copenhagen. Her research interests are in the broad areas of medical imaging, representation learning and data science. She has experience developing algorithms to deal with label noise, limited amounts of data and class-imbalance, problems that are fairly common in medical datasets. She is working on the project “Making Metadata Count”. Before joining ITU, she received a degree in Telecommunications Engineering from the University of Granada, a Master of Science in Biomedical Computing from the Technical University of Munich and her Ph.D. from Pompeu Fabra University. Find more info on


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    Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
    Dovile JuodelyteYucheng LuAmelia Jiménez-Sánchez, Sabrina Bottazzi , Enzo Ferrante , and 1 more author
    arXiv preprint arXiv:2403.04484, 2024
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    [Citation needed] Data usage and citation practices in medical imaging conferences
    Théo Sourget, Ahmet Akkoç , Stinna Winther , Christine Lyngbye Galsgaard , Amelia Jiménez-Sánchez, and 3 more authors
    arXiv preprint arXiv:2402.03003, 2024
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    Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
    Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona , Dovile JuodelyteThéo Sourget, Caroline Vang-Larsen , and 2 more authors
    arXiv preprint arXiv:2402.06353, 2024
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    Revisiting Hidden Representations in Transfer Learning for Medical Imaging
    Transactions on Machine Learning Research, 2023
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    Augmenting Chest X-ray Datasets with Non-Expert Annotations
    Cathrine Damgaard , Trine Naja Eriksen , Dovile JuodelyteVeronika Cheplygina, and Amelia Jiménez-Sánchez
    arXiv preprint arXiv:2309.02244, 2023
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    Memory-aware curriculum federated learning for breast cancer classification
    Amelia Jiménez-Sánchez, Mickael Tardy , Miguel A González Ballester , Diana Mateus , and Gemma Piella
    Computer Methods and Programs in Biomedicine, 2022
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    Detecting Shortcuts in Medical Images – A Case Study in Chest X-rays