Pedersen, N., Sydendal, R., Wulff, A., Raumanns, R., Petersen, E., & Cheplygina, Robustness and sex differences in skin cancer detection: logistic regression vs CNNs. ArXiv preprint arXiv:2504.11415v1, 2025.
Raumanns, R., Schouten, G., Pluim, J. P. W. & Cheplygina, Dataset Distribution Impacts Model Fairness: Single Vs. Multi-task Learning. In Ethics and Fairness in Medical Imaging, 2024.
Raumanns, R., Schouten, G., Joosten, M., Pluim, J. P. W. & Cheplygina, ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification. MELBA journal 1, 1–26, 2021.
Abbasi-Sureshjani, S., Raumanns, R., Michels, B. E. J., Schouten, G. & Cheplygina, Risk of Training Diagnostic Algorithms on Data with Demographic Bias. In Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020.
Raumanns, R., Contar, E. K., Schouten, G. & Cheplygina, Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis. ArXiv preprint arXiv:2004.14745, 2020