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使用自然语言处理对放射学报告进行自动标签:传统方法与新方法的比较

Seo Yi Chng¹, Paul J W Tern², Matthew R X Kan³

¹Department of Paediatrics National University of Singapore Singapore Singapore.

Health care science

|June 28, 2024

概括

使用自然语言处理 (NLP) 对放射学报告的自动标记有助于创建用于人工智能模型培训的基础真相数据集. 与其他NLP方法相比,特定领域的BERT模型为此任务提供了更高的性能.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
自然语言处理自然语言处理.

背景情况:

放射学研究的大型数据集对于训练计算机视觉模型至关重要.
放射学报告的自动标签对于生成地面真相数据至关重要.
自然语言处理 (NLP) 技术是自动化标签过程的关键.

研究的目的:

解释用于自动化放射学报告标签的数据预处理步骤.
审查和比较不同自动标签方法的性能.
为了确定放射学报告注释中最有效的NLP方法.

主要方法:

基于规则的文本匹配算法.
传统的机器学习模型 (需要令牌化和矢量化).
深度学习模型 (例如,长短期记忆网络).
基于变压器的模型,特别是来自变压器的双向编码器表示 (BERT).

主要成果:

基于规则的方法可以获得高的F1分数,但需要小心处理否定.
传统和深度学习模型在大型培训数据集上表现良好.
伯特模型,特别是特定领域的模型,需要更少的数据进行微调,并实现更高的性能.

结论:

放射学报告的自动标签是可行的,使用各种NLP技术.
由于其效率和性能,BERT模型代表了自动化放射学报告标签的最先进状态.
有效的数据预处理和模型选择对于成功的自动注释至关重要.

关键词:

自动化标签的自动化标签机器学习是机器学习.自然语言处理自然语言处理.神经网络的神经网络的神经网络放射学放射学是指放射学

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