联合剂量预测框架,用于基于知识的辐射疗法规划
Jingyun Chen1, Martin King2, Yading Yuan1,3
1Department of Radiation Oncology, Columbia University Irving Medical Center 622 W 168th St, New York, NY 10032, United States.
Proceedings of SPIE--the International Society for Optical Engineering
|December 31, 2025
概括
联合学习 (FL) 允许深度学习模型的协作训练用于剂量预测,而无需共享患者数据. 在理想的条件下,FL的表现优于个人培训,并与集中培训相匹配,显示出医疗数据协作的前景.
科学领域:
- 医学物理 医学物理
- 人工智能的人工智能
- 辐射疗法 辐射疗法
背景情况:
- 剂量预测对于放射治疗中的基于知识的规划 (KBP) 至关重要.
- 深度学习方法需要大量的数据集,需要共享数据.
- 联合学习 (FL) 为协作模式培训提供了一种保护隐私的方法.
研究的目的:
- 评估联合学习 (FL) 的性能,以基于深度学习的剂量预测.
- 将FL与集中式和个人培训策略进行比较.
- 评估数据分布 (IID与非IID) 对FL业绩的影响.
主要方法:
- 开发了FedKBP框架来训练剂量预测模型.
- 模拟FL和个人培训,使用OpenKBP数据集中的8个站点.
- 实现独立且相同分布 (IID) 和非IID数据分布,以测试模型的稳定性.
主要成果:
- 在速度和准确性方面,FL始终优于个人训练.
- 在IID条件下,FL取得了与集中培训相美的绩效.
- 非IID数据分布导致绩效差异,较大的网站高达19%的表现优于较小的网站.
结论:
- 联合学习是一种可行的,保护隐私的替代方案,可以替代用于剂量预测的集中训练.
- 在FL (非IID) 中的数据异质性需要先进的方法,而不仅仅是简单的平均.
- 通过FL的合作对于改善放射治疗规划中的剂量预测准确性至关重要.
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