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放射学中的图像注释和策划:用于机器学习从业者的概述

Fabio Galbusera¹, Andrea Cina^2,3

¹Spine Center, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland. fabio.galbusera@kws.ch.

European radiology experimental

|February 5, 2024

概括

高质量的数据对于医疗成像中可靠的人工智能 (AI) 是至关重要的. 本综述详细介绍了用于人工智能开发的方法和免费软件,用于准备一致,标准化和非识别的放射性数据集.

科学领域:

医疗成像医学成像
人工智能的人工智能
数据科学数据科学数据科学

背景情况:

机器学习 (ML) 和人工智能 (AI) 模型的有效性在很大程度上取决于培训数据的质量.
不一致,非标准化或不正确地去识别的数据可能导致不可靠的AI算法,特别是在放射学等敏感领域.

研究的目的:

提供技术和自由可用的软件解决方案的全面概述,以确保在放射学中的机器学习应用程序中提供高质量的数据.
引导研究人员准备一致,标准化,可追溯,正确注释和去识别的数据集,以遵守数据保护法规.

主要方法:

审查基本的医学成像概念 (分辨率,像素深度) 和数据存储格式.
探索用于图像处理的自由软件 (例如,ImageJ,3D Slicer) 和匿名化技术.
讨论患者隐私保护方法,包括匿名化和假名化,遵守GDPR和HIPAA.
图像注释工具和数据协调/规范化技术的概述.

主要成果:

确定了人工智能数据质量的关键考虑因素,包括标准化,可追溯性和非识别性.
强调了免费软件解决方案在医疗图像处理和注释方面的实用性.
强调了监管合规 (GDPR,HIPAA) 在数据准备中的重要性.
证明了从放射图像中去除识别特征的方法.

结论:

实施强大的数据策划策略对于开发可靠和符合X射线学AI至关重要.
利用自由可用的软件工具可以显著促进为人工智能研究准备高质量的数据集.
在整个数据集准备生命周期中,遵守数据隐私法规至关重要.

关键词:

人工智能的人工智能是人工智能.数据策划数据的策划.图像处理 (计算机辅助)机器学习是机器学习.隐私隐私隐私隐私隐私隐私

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