VinDr-Mammo: A large-scale benchmark dataset for computer-aided detection and diagnosis in full-field digital mammography

Breast cancer is one of the most prevalent types of cancer and the leading type of cancer death. Mammography is the recommended imaging modality for periodic breast cancer screening. A few datasets have been published to develop computer-aided tools for mammography analysis. However, these datasets either have a limited sample size or consist of screen-film mammography (SFM), which have been replaced by full-field digital mammography (FFDM) in clinical practices. This project introduces a large-scale full-field digital mammography dataset of 5,000 four-view exams, which are double read by experienced mammographers to provide cancer assessment and breast density following the Breast Imaging Report and Data System (BI-RADS). Breast abnormalities that require further examination are also marked by bounding rectangles.乳腺癌是全球范围内最为高发的癌症类型之一，亦是癌症相关死亡的首要病因。乳腺钼靶成像（Mammography）是临床推荐用于周期性乳腺癌筛查的影像检查手段。目前已有若干公开数据集用于开发乳腺钼靶分析的计算机辅助工具，但此类数据集要么样本量受限，要么基于屏片钼靶成像（SFM）构建——该技术目前已在临床实践中被全视野数字化乳腺钼靶成像（FFDM）所取代。本项目构建了一套大规模全视野数字化乳腺钼靶数据集，涵盖5000例四体位乳腺检查病例，由资深乳腺影像医师采用双读片模式完成标注，依据乳腺影像报告与数据系统（BI-RADS）标准开展癌症风险评估与乳腺密度分级。对于需进一步排查的乳腺异常病灶，研究人员同时通过边界矩形框完成了标注。

搜集汇总

背景与挑战

背景概述

VinDr-Mammo是一个大规模全视野数字乳腺X线摄影基准数据集，包含5,000个四视图检查，专为计算机辅助检测和诊断设计。它由经验丰富的乳腺放射科医生双读，提供基于BI-RADS标准的癌症评估、乳腺密度标注以及异常区域的边界框标记，弥补了现有数据集样本小或使用淘汰技术的不足。