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-center 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.

翻译：乳腺癌的早期检测对于改善患者预后至关重要。虽然乳腺X线摄影仍是主要的筛查手段，但对于致密型乳腺组织女性和高风险人群，磁共振成像（MRI）正日益被推荐作为补充检查工具。然而，多参数乳腺MRI的采集与判读耗时较长且需要专业知识，限制了其在临床实践中的可扩展性。人工智能方法在辅助乳腺MRI判读方面展现出潜力，但其发展受限于大型、多样化且可公开获取数据集的稀缺性。为填补这一空白，我们提出了一个公开可用的多中心乳腺MRI数据集，该数据集采集自五个欧洲国家的六家临床机构。该数据集包含741例接受筛查或诊断性乳腺MRI检查的女性病例，涵盖恶性、良性及非病灶病例。数据采集使用了异构的扫描设备、场强和采集协议，反映了真实临床环境中的多样性。此外，我们通过基于Transformer模型的基准实验展示了该数据集的潜在应用场景，并为未来的方法学比较提供了参考性能指标。