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集成嵌套交叉验证,自动化超参数优化,高性能计算,以减少和量化深度学习模型测试性能估计的差异
Paul Calle1, Averi Bates1, Justin C Reynolds1
1School of Computer Science, University of Oklahoma, Norman, 73019, OK, USA.
Computer methods and programs in biomedicine
|September 14, 2025
概括
NACHOS是一个使用嵌套交叉验证和自动超参数优化的新框架,可以减少和量化医疗成像深度学习模型中的性能差异. 这提高了对现实世界的部署的可信度.
科学领域:
- 医学成像医学成像
- 深度学习是一种深度学习.
- 计算科学是一种计算科学.
背景情况:
- 医学成像中的深度学习模型性能基准测试存在变化和偏差,妨碍了现实世界的信任.
- 使用固定测试集的当前方法不充分捕捉性能指标差异.
- 这就需要强大的评估框架来实现可靠的医疗人工智能部署.
研究的目的:
- 引入NACHOS (使用超级计算进行嵌套和自动交叉验证和超级参数优化) 以减少和量化性能指标差异.
- 在医学成像中开发一个可靠的深度学习模型评估框架.
- 提高AI工具在临床环境中的可靠性.
主要方法:
- 在一个并行高性能计算 (HPC) 框架内,NACHOS集成了嵌套交叉验证 (NCV) 和自动化超参数优化 (AHPO).
- 在胸部X射线和光学一致性断层扫描 (OCT) 数据集上证明了这一点.
- 引入了DACHOS (使用超级计算实现自动交叉验证和超参数优化部署) 以在完整的数据集上构建最终模型.
主要成果:
- 在量化和减少估计差异方面,NCV至关重要.
- AHPO确保在测试折叠中始终保持超参数优化.
- 高性能计算保证了拟议框架的计算可行性.
结论:
- NACHOS和DACHOS提供了一个可扩展,可复制和可靠的框架,用于深度学习模型的评估和部署在医学成像中.
- 开源代码库是公开的,以促进采用和进一步的研究.
- 该框架解决了对医疗保健中可靠人工智能的关键需求.
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