适应性规范化增强了深度学习模型在胸部X射线分类中的概括性
Jatsada Singthongchai1, Tanachapong Wangkhamhan1
1Department of Computer Science and Information Technology, Faculty of Science and Health Technology, Kalasin University, Kalasin 46000, Thailand.
Journal of imaging
|January 27, 2026
概括
适应式预处理管道改善了胸部X射线分类的准确性和数据集的稳定性. 立体图标标准化是关键,在最小的计算成本下提供实用的好处.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 胸部X射线 (CXR) 分析在很大程度上依赖于图像预处理来进行准确的分类.
- 标准的规范化技术可能无法最佳地处理CXR对比度和采集的变化.
- 开发强大的预处理方法对于医疗诊断中可靠的AI至关重要.
研究的目的:
- 将自适应预处理与CXR分类的标准方法进行比较.
- 评估直方图标准化和ROI裁剪对模型性能的影响.
- 评估适应管道的计算效率和统计意义.
主要方法:
- 对最小-最大缩放,Z分数规范化和自适应管道 (ROI裁剪+直方图标准化) 的受控基准测试.
- 在四个公共CXR数据集和三个卷积神经网络架构中进行评估.
- 使用弗里德曼-涅梅尼和威尔科克森签名等级测试进行统计分析.
主要成果:
- 适应管道通常提高了准确性和F1分数,特别是在具有稳定对比度的数据集上.
- 历史图标标准化是提高绩效的主要驱动力,ROI作物提供了补充的好处.
- 观察到最小的计算开销 (+6.3%的训练时间;5.2 ms/批).
结论:
- 适应性规范化,特别是直方图标准化,是CXR分类的实际预处理选择.
- 拟议的管道提高了人工智能驱动的CXR分析中跨数据集的稳定性和可靠性.
- 该方法在微不足道的计算成本下提供了统计学上显著的性能增长.
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