神经网络-调查的乳腺扫描数据集
Adam Mračko1,2, Lucia Vanovčanová3,4, Ivan Cimrák1,2
1Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia.
Journal of imaging
|May 26, 2023
概括
这项研究调查了乳房学数据库,以进行深度学习. 它确定了像CBIS-DDSM和MIAS这样的关键数据集,这对于训练人工智能在乳腺癌检测方面至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 深度神经网络 (DNN) 越来越多地用于哺乳镜分析.
- 训练DNN需要大量的高质量的乳房学数据集,并注释出异常.
- 开放式数据库是开发人工智能用于乳腺癌查的重要资源.
研究的目的:
- 进行对适合培训DNN的开放式乳房学数据库的全面调查.
- 识别含有具有定义异常感兴趣区域的图像的数据库.
- 审查使用这些数据库的研究,并与DNN报告其结果.
主要方法:
- 对乳房镜像数据库的系统审查.
- 纳入标准侧重于开放访问的可用性和注释的异常区域的存在.
- 在文献中搜索使用这些数据库与DNN进行乳房影像分析的研究.
主要成果:
- 确定了一些关键数据库:INbreast,CBIS-DDSM,OMI-DB和MIAS.
- 这些数据库总共提供至少3801张独特的图像,其中有1842名患者的4125个发现.
- 通过与OPTIMAM的协议,有可能访问最多14474名患者的数据.
- 详细说明了乳房图像的注释过程.
结论:
- 已建立的乳腺癌数据库提供了培训DNN进行乳腺癌检测的基本数据.
- 调查的资源为AI开发提供了大量注释图像.
- 了解图像注释对于最大限度地提高这些数据集的效用至关重要.
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