DUDE:使用变量nEighbors进行生理时间序列分析的深度无监督域适应.
Jeremy Levy1, Noam Ben-Moshe2, Uri Shalit3
1Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and the Faculty of Biomedical Engineering, Technion, Israel Institute of Technology (Technion-IIT), Haifa, Israel.
Physiological measurement
|November 20, 2025
概括
使用变量nEighbors (DUDE) 的深度无监督域适应通过解决数据分布转移来改善生理信号的深度学习. 这种新的框架提高了模型在现实世界医疗应用中的通用性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 深度学习的优势在于对心电图等生理信号的数据分布一致.
- 由于培训和部署数据之间的分布转移,现实世界的部署面临挑战.
- 不重叠的源代码和目标域支持对模型概括构成了重大障碍.
研究的目的:
- 引入一个新的框架,使用变量nEighbors (DUDE) 进行深度无监督域适应,用于连续的生理信号分析.
- 为了应对源和目标数据分布显著不同的地方域调整的挑战.
- 提高深度学习模型在医疗应用中的通用性.
主要方法:
- 在源域和目标域之间开发了一种新的对比损失函数.
- 实施了动态邻居选择策略,根据潜在空间密度适应性地确定邻居.
- 利用了多个现实世界数据集,具有多种目标领域特征 (人口统计,种族,地理,共同疾病).
主要成果:
- 与现有的基线方法相比,DUDE 显示出更高的性能.
- 与视觉表示的近邻对比学习策略相比,实现了高达16%的改进.
- 在现实世界数据集中验证了有效性,具有显著的域名转移.
结论:
- DUDE有效地弥合了医疗应用领域的领域适应差距.
- 该框架显示了提高诊断工具的精度和适应性的潜力.
- 这项工作通过改进模型通用性,为更强大的患者护理人工智能铺平了道路.
相关概念视频
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