BUS-BRA:用于评估计算机辅助诊断系统的乳房超声波数据集
Wilfrido Gómez-Flores1, Maria Julia Gregorio-Calas2, Wagner Coelho de Albuquerque Pereira3
1Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Tamaulipas, Mexico.
Medical physics
|November 8, 2023
概括
本研究介绍了BUS-BRA,这是一个新的,公开可用的乳房超声数据集,包含详细的注释,用于开发和评估用于乳房损伤检测和分类的计算机辅助诊断 (CAD) 系统.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 计算机辅助诊断 (CAD) 系统通过使用乳腺超声波 (BUS) 提高乳腺癌查效率.
- 开发有效的CAD系统需要全面的,注释的BUS数据集,包括病变细分,活检结果和BI-RADS分类.
- 标准化数据分区对于可重现的研究和CAD方法的公平比较至关重要.
研究的目的:
- 为了介绍BUS-BRA,一个新的,公开可用的乳房超声波数据集.
- 为CAD系统开发提供注释案例的大量增加.
- 为了客观评估和比较CAD系统,包括标准化分区.
主要方法:
- 该数据集包括来自1064名女性患者的1875张匿名BUS图像,其中722个是良性瘤和342个是恶性瘤.
- 专家注释包括手动损伤细分,BI-RADS评估 (2-5类) 和标准化的5倍和10倍交叉验证分区.
- 使用深度神经网络实施了评估框架,以评估数据集对损伤细分和分类的有用性.
主要成果:
- BUS-BRA数据集为病变细分和分类提供了广泛的注释.
- 标准化分区有助于对CAD系统进行可重复的评估.
- 数据集的实用性通过深度神经网络性能评估来验证.
结论:
- BUS-BRA数据集支持开发和评估基于AI的方法,用于在BUS图像中检测,细分和分类乳腺病变.
- 潜在的应用包括图像处理增强和功能工程,以改进图像描述.
- 这个资源通过人工智能促进了乳腺癌诊断的进步.
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