Early detection of breast cancer is critical for improving patient outcomes. While mammography remains the primary screening modality, magnetic resonance imaging (MRI) is increasingly recommended as a supplemental tool for women with dense breast tissue and those at elevated risk. However, the acquisition and interpretation of multiparametric breast MRI are time-consuming and require specialized expertise, limiting scalability in clinical practice. Artificial intelligence (AI) methods have shown promise in supporting breast MRI interpretation, but their development is hindered by the limited availability of large, diverse, and publicly accessible datasets. To address this gap, we present a publicly available, multi-centre breast MRI dataset collected across six clinical institutions in five European countries. The dataset comprises 741 examinations from women undergoing screening or diagnostic breast MRI and includes malignant, benign, and non-lesion cases. Data were acquired using heterogeneous scanners, field strengths, and acquisition protocols, reflecting real-world clinical variability. In addition, we report baseline benchmark experiments using a transformer-based model to illustrate potential use cases of the dataset and to provide reference performance for future methodological comparisons.
@article{arxiv.2506.00474,
title = {A European Multi-Center Breast Cancer MRI Dataset},
author = {Gustav Müller-Franzes and Lorena Escudero Sánchez and Nicholas Payne and Alexandra Athanasiou and Michael Kalogeropoulos and Aitor Lopez and Alfredo Miguel Soro Busto and Julia Camps Herrero and Nika Rasoolzadeh and Tianyu Zhang and Ritse Mann and Debora Jutz and Maike Bode and Christiane Kuhl and Yuan Gao and Wouter Veldhuis and Oliver Lester Saldanha and JieFu Zhu and Jakob Nikolas Kather and Daniel Truhn and Fiona J. Gilbert},
journal= {arXiv preprint arXiv:2506.00474},
year = {2026}
}