混合框架用于自动生成乳腺造影放射学报告的混合框架
Eduardo Godoy1,2,3, Diego Mellado3,4, Joaquin de Ferrari2,5
1PhD program in Applied Informatics Engineering, School of Informatics Engineering, Universidad de Valparaíso, Valparaíso, Chile.
Computational and structural biotechnology journal
|July 31, 2025
概括
这项研究提出了一个深度学习框架,用于自动化对乳房镜报告的临床文本生成,以帮助放射科医生. 该系统提供诊断建议和验证证据,提高乳腺癌查效率.
科学领域:
- 医疗成像医学成像
- 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:
- 乳腺癌是影响妇女健康和生产力的重大健康问题.
- 深度学习 (DL) 的进步正在自动化放射性报告的生成.
- 现有的方法可能无法完全解决图像质量变化或简化放射科医生的工作流程.
研究的目的:
- 引入一个框架,用于自动化临床文本生成在乳房影像.
- 通过提供预处理的证据和诊断建议,协助放射科医生.
- 评估图像增强和特征提取对报告生成的影响.
主要方法:
- 使用编码器-解码器架构用于自然语言生成 (NLG) 模型.
- 在西班牙放射学文本库上训练和微调模型.
- 包含图像强度增强和命名实体识别 (NER) 技术.
主要成果:
- 展示了基于医学图像的自动化临床报告生成框架.
- 通过比较NLG指标分析确定了最佳特征提取方法.
- 展示了NER在精度评估自动化方面的潜力.
结论:
- 拟议的框架是系统化自动化临床报告生成的可行起点.
- 该系统通过提供验证的诊断建议来支持放射科医生.
- 进一步的开发可以提高乳房造影报告的效率和准确性.
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