软标签引导变压器用于放射学报告的生成
Xinyao Liu1, Junchang Xin2, Qi Shen1
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
Journal of biomedical informatics
|October 15, 2025
概括
这项研究引入了一种软标签引导变压器 (SLGT),以改进自动放射学报告生成. SLGT模型更好地模拟放射科医生的过程,从而更准确地描述医学图像,并帮助计算机辅助诊断.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 放射学报告对于医生的决策至关重要.
- 自动报告生成可以提高效率,但往往缺乏细节.
- 目前的方法很难准确地描述特定的病变特征.
研究的目的:
- 开发一种自动化放射学报告生成方法,模仿放射科医生的诊断过程.
- 提高生成的放射学报告的准确性和细节性,特别是异常发现.
主要方法:
- 提出了一个软标签引导变压器 (SLGT) 模型.
- 采用软标签引导的注意力机制,专注于与疾病相关的特征.
- 调整图像和文本特征,并使用生成的文本来指导表示.
- 使用混合损失函数来生成文本,疾病分类和视觉文本对齐.
主要成果:
- 在IU X-ray,MIMIC-CXR和COV-CTR数据集上评估了SLGT模型.
- 与现有最先进的模型相比,SLGT在所有测试的数据集中表现出更高的性能.
结论:
- 开发的SLGT模型显著提高了自动放射学报告的生成.
- 这一进步使得自动报告在计算机辅助诊断应用中变得更加可行.
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