联合不平衡适应放射学报告生成
Yuexin Wu1, I-Chan Huang2, Xiaolei Huang1
1Department of Computer Science, University of Memphis, Memphis, 38152, TN, United States.
Research square
|September 11, 2024
概括
放射学报告生成中的数据不平衡阻碍了准确性. 吉马模型通过适应代币和标签不平衡来解决这一问题,改善临床报告的生成,特别是对于罕见发现.
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:
- 放射学报告生成将医学图像转化为临床描述.
- 数据不平衡,包括不常见的医疗令牌和正常发现的流行,挑战报告生成模型.
- 现有的研究很少解决这些特定的不平衡问题.
研究的目的:
- 为了解决放射学报告生成中的数据不平衡挑战.
- 为了提高自动化放射学报告的稳定性和准确性.
- 为了减轻由不常见的代币和异常标签引起的低绩效.
主要方法:
- 提出了一个联合失衡适应 (JIMA) 模型.
- 利用代币和标签不平衡来提高任务的稳定性.
- 采用了难以轻松的学习策略,专注于罕见的标签和临床代币.
- 从图像中预测实体分布以生成报告.
主要成果:
- 在IUX射线和MIMIC-CXR数据集上取得了显著的改善 (平均为16.75% - 50.50%).
- 证明了对不常见的代币和异常标签的增强处理.
- 人类评估和案例研究证实了产生更准确的临床报告.
结论:
- 数据不平衡显著影响放射学报告生成性能.
- 拟议的课程学习策略有效地减少了数据不平衡的影响.
- JIMA方法为改善自动化医疗报告生成提供了一个有希望的方向.
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