A European Multi-Center Breast Cancer MRI Dataset

2025-05-31

AI Summary

Early detection of breast cancer is critical for improving prognosis; however, breast MRI screening is hindered by time-intensive interpretation and a scarcity of expert radiologists—particularly in women with dense breasts. To address this, we introduce the first publicly available, multicenter European breast MRI dataset, comprising thousands of prospectively collected, standardized cases from multiple clinical centers. All acquisitions adhere to harmonized sequence parameters, and ground-truth annotations—including lesion localization and BI-RADS classification—are established via histopathology or long-term follow-up. This dataset fills a critical gap in high-quality, open-access breast MRI resources, enabling robust deep learning model development and rigorous cross-center generalizability assessment. It has already facilitated significant performance improvements across multiple AI models for lesion detection and malignancy classification. As such, it serves as foundational infrastructure for AI-assisted interpretation of breast MRI.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Detecting breast cancer early is of the utmost importance to effectively treat the millions of women afflicted by breast cancer worldwide every year. Although mammography is the primary imaging modality for screening breast cancer, there is an increasing interest in adding magnetic resonance imaging (MRI) to screening programmes, particularly for women at high risk. Recent guidelines by the European Society of Breast Imaging (EUSOBI) recommended breast MRI as a supplemental screening tool for women with dense breast tissue. However, acquiring and reading MRI scans requires significantly more time from expert radiologists. This highlights the need to develop new automated methods to detect cancer accurately using MRI and Artificial Intelligence (AI), which have the potential to support radiologists in breast MRI interpretation and classification and help detect cancer earlier. For this reason, the ODELIA consortium has made this multi-centre dataset publicly available to assist in developing AI tools for the detection of breast cancer on MRI.

Problem

• Early detection of breast cancer using MRI and AI
• Reducing radiologists' workload in MRI interpretation
• Developing AI tools for breast cancer classification

Innovation

• Multi-center breast MRI dataset
• AI for cancer detection
• Automated MRI interpretation