批量大小:去大或回家? 在医疗自动编码器中,具有较小批量尺寸的反直觉改进
Cailey I Kerley1, Leon Y Cai2, Yucheng Tang1
1Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
概括
较小的批量尺寸可以提高电子健康记录和医学成像的自动编码器性能. 这导致了更有意义的潜空间,改善了生物数据洞察力和模型准确性.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 批量大小是深度学习中的关键超参数.
- 传统上,更大的批量被认为可以提高模型性能.
- 自动编码器用于从复杂数据中导出潜在空间.
研究的目的:
- 为了研究批量大小对自动编码器性能的影响.
- 为了确定较小的批量是否改善了EHR和医疗成像数据的潜在空间质量.
- 评估不同批量大小衍生的潜空间的生物学意义.
主要方法:
- 在EHR和医学成像数据集上训练有素的完全连接和卷积自动编码器.
- 从1到100变化的批量大小,同时保持其他超参数不变.
- 使用重建损失,潜伏空间分析和下游任务准确度评估性能.
主要成果:
- 较小的批量大小始终改善了这两个数据集的损失性能.
- 来自较小批量的潜空间捕获了更多与生物学相关的信息,例如性别和瘤横向性.
- 下游分类和回归任务显示,在较小的批量大小下,统计学上有显著的改善.
结论:
- 较小的批量大小有利于对电子病历和医学成像数据的自动编码器进行训练.
- 这种方法产生了更多的信息潜伏空间,增强了生物洞察力.
- 这些发现挑战了关于深度学习中批量大小优化的传统智慧.
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