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AERMNet:注意力增强的关系记忆网络用于医疗图像报告生成
Xianhua Zeng1, Tianxing Liao1, Liming Xu2
1College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Computer methods and programs in biomedicine
|December 19, 2023
概括
注意增强关系记忆网络 (AERMNet) 模型通过加强单词相关性和保存上下文来改善医疗图像报告生成,从而导致更准确的诊断.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 自然语言处理自然语言处理.
背景情况:
- 医疗图像诊断报告的自动生成通过减少工作量和提高诊断效率和准确性来帮助临床医生.
- 现有的报告生成模型存在词汇相关性较弱和缺乏上下文信息的问题.
研究的目的:
- 为了解决当前医学图像报告生成模型的局限性.
- 提出一种改进词汇相关性和上下文信息利用的新型模型.
主要方法:
- 开发了一个注意力增强的关系记忆网络 (AERMNet) 模型.
- 包含一个关系记忆模块,通过先前生成的单词进行更新,以加强单词间的相关性.
- 使用双 LSTM 与交互模块来最大限度地减少上下文损失和最大限度地利用功能信息.
主要成果:
- 在四个医学数据集 (胎儿心脏,超声波,IUX射线,MIMIC-CXR) 上,AERMNet表现出卓越的性能.
- 在语言生成指标中观察到显著的改善,包括FH上2.4%的Cider增加,超声波上2.4%的Bleu1,IUX射线上16.4%的Cider和MIMIC-CXR上9.7%的Bleu2.
- 该模型为医学图像报告生成更准确的疾病信息.
结论:
- 拟议的AERMNet模型推进了医学图像报告生成领域.
- 这项工作扩大了计算机辅助诊断的应用前景.
- 对于AERMNet的代码是公开可用的,用于进一步的研究和开发.
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