TN-Mammo: A Multi-view Mammography Dataset for Breast Density Classification

Breast cancer is one of the most common types of cancer among women, leading to a growing and essential need for early and precise detection. A variety of machine learning techniques have been demonstrating great promise in improving diagnostic accuracy integrated with digital mammography, which remains the gold standard screening technique in the detection of early breast cancer. Since the performance of machine learning models generally depends on the quality of training data, it is essential to build a high-quality mammogram dataset. Although there exists a number of public datasets, there are still significant limitations due to both their quality and quantity. We present an extensive Vietnamese mammogram dataset with breast density annotations in an effort to bridge the gap between availability and practical usability. Our mammogram dataset named TNMammo, consists of bilateral craniocaudal (CC) and mediolateral oblique (MLO) views for 676 subjects, each with paired left and right breast views. The breast density and orientation for each case have been independently assessed by two radiologists in a double-blind manner, ensuring consistency and reliability in the annotations. Each case is evaluated based on breast density, categorized into four levels: A, B, C, and D.