使用监督学习的放射学报告的多语言RECIST分类
Luc Mottin1,2, Jean-Philippe Goldman3, Christoph Jäggli4
1HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland.
Frontiers in digital health
|June 30, 2023
概括
这项研究探讨了AI和NLP用于从放射学报告中自动进行RECIST分类. 最好的模型实现了高精度,可与专家标签相提并论,并且对新数据进行了很好的概括.
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:
- 准确的瘤反应评估对于癌症治疗至关重要.
- 手动从放射学报告中进行RECIST分类可能是耗时且主观的.
- 自动化RECIST分类可以提高效率和一致性.
研究的目的:
- 探索人工智能和NLP技术用于自动RECIST分类.
- 评估语言 (法语,德语) 和机构因素对分类质量的影响.
- 评估开发模型的概括性和准确性.
主要方法:
- 评估了7种用于基线性能的机器学习方法.
- 为法语和德语语言开发和微调模型.
- 将模型性能与专家注释进行比较,使用F1得分,马修的相关系数和科恩的卡帕等指标.
主要成果:
- 在2类 (进步/非进步) 中获得了90%的F1平均分数,在4类RECIST分类中获得了86%的F1分数.
- 性能指标与手动标签具有竞争力 (MCC 79%,卡帕 76%).
- 在未见的数据上证明模型的概括性.
结论:
- 人工智能和NLP技术有效地支持自动RECIST分类.
- 语言和机构的特殊性对分类质量有可管理的影响.
- 预训练的语言模型 (PLM) 可以提高分类器的准确性.
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