Publications

Abbasi-Sureshjani, S., Raumanns, R., Michels, B. E. J., Schouten, G. & Cheplygina, V. 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, V. Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis. arXiv preprint arXiv:2004.14745, 2020

Raumanns, R., Schouten, G., Joosten, M., Pluim, J. P. W. & Cheplygina, V. ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification. MELBA journal 1, 1–26 (2021).

Raumanns, R., Schouten, G., Pluim, J. P. W. & Cheplygina, V. Dataset Distribution Impacts Model Fairness: Single Vs. Multi-task Learning. in Ethics and Fairness in Medical Imaging, 2024.