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优化器的评估及其在肺部瘤自我细分中的性能

Prabhakar Ramachandran¹, Tamma Eswarlal², Margot Lehman¹

¹Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.

Journal of medical physics

|August 14, 2023

概括

亚当和NAdam优化器在非小细胞肺癌自分化方面表现最好. 然而,由于数据异质性,模型性能在临床数据集上显著下降,这凸显了需要仔细选择优化器的需要.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

优化器对于深度学习模型至关重要,特别是在医学图像分析中.
准确的非小细胞肺癌 (NSCLC) 目标体积的自细分对于有效的放射治疗计划至关重要.

研究的目的:

在胸部CT图像上评估NSCLC自分化的六种不同优化器的准确性.
使用各种细分指标来比较优化器的性能.

主要方法:

利用112名患者 (92名来自TCIA,20名来自当地诊所) 进行培训和测试nnU-Net模型.
调查了六个优化器:AdaDelta,AdaGrad,Adam,NAdam,RMSprop和SGD. 这些优化器是最重要的.
使用子相似系数 (DSC),贾卡德指数,灵敏度,精度,豪斯多夫距离 (HD),HD95和ASSD评估性能.

主要成果:

亚当 (0.85) 和NAdam (0.84) 在TCIA测试数据上获得了最高的DSC.
当将TCIA训练的模型应用于本地临床数据集时,观察到DSC,HD,HD95和ASSD的统计学显著下降.
这种性能下降表明数据集之间的图像和/或掩盖异质性.

结论:

优化器的选择显著影响深度学习自分割性能.
当应用到新的临床数据集时,优化器的行为可能会发生变化,影响模型的概括性.
精心选择优化器对于在临床应用中实现最佳性能和通用性至关重要.

关键词:

自动细分汽车细分计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.优化算法优化算法

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