MammosighTR: Nationwide Breast Cancer Screening Mammogram Dataset with BI-RADS Annotations for Artificial...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Radiology
• Oncology

Background:

• Breast cancer screening is crucial for early detection.
• Developing robust artificial intelligence (AI) models requires large, well-annotated datasets.
• Existing datasets may not fully represent diverse populations or screening protocols.

Purpose of the Study:

• To introduce and describe the MammosighTR dataset.
• To provide a valuable resource for AI model development in breast cancer detection.
• To facilitate research on AI-driven mammography analysis.

Main Methods:

• The MammosighTR dataset was curated from Türkiye's national breast cancer screening mammography program.
• Mammograms are labeled according to the Breast Imaging Reporting and Data System (BI-RADS).
• Detailed annotations include breast composition and lesion location within quadrants.

Main Results:

• The dataset contains a significant collection of mammograms suitable for AI training and testing.
• Annotations provide granular information for precise model evaluation.
• The dataset's origin ensures relevance to a specific national screening context.

Conclusions:

• The MammosighTR dataset is a valuable contribution to the field of AI in medical imaging.
• It offers a unique resource for advancing AI-powered breast cancer detection tools.
• This dataset can accelerate the development and validation of AI algorithms for mammography.