在域转移下实现可靠的WMH细分:使用最大调整的应用研究,以改善不确定性估计
Franco Matzkin1, Agostina Larrazabal2, Diego H Milone1
1Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL, Santa Fe, Argentina.
Computers in biology and medicine
|July 4, 2025
概括
最大度调整通过提高不确定性估计来增强白质超强度 (WMH) 分段. 这有助于在不同的临床环境中识别细分错误,而不需要基本真相标签.
科学领域:
- 医学成像分析分析 医学成像分析
- 深度学习用于神经成像.
背景情况:
- 精确的白质超强度 (WMH) 分段对于诸如多发性硬化症等神经疾病至关重要.
- 磁共振成像数据的域位移 (例如,不同的机器或参数) 挑战模型校准和不确定性估计.
- 预测不确定性可以作为一个代理来识别部署后的错误,而没有基准真相标签.
研究的目的:
- 调查域位移对WMH细分精度的影响.
- 评估最大度规范化技术,以提高模型校准和不确定性估计在WMH细分.
- 在临床部署中使用预测不确定性识别潜在的细分错误.
主要方法:
- 使用U-Net架构进行WMH细分.
- 应用和评估的最大规范化方案.
- 在两个公共数据集上进行了测试:WMH细分挑战和3D-MR-MS.
- 使用子系数,豪斯多夫距离,预期校准误差和基于的不确定性来评估性能.
主要成果:
- 基于的不确定性估计有效地预测跨不同数据分布的细分错误.
- 最大度规范化加强了不确定性和细分性能之间的联系.
- 在域转移条件下,模型校准得到了改进.
结论:
- 最大度规范化增强了WMH细分的不确定性估计,特别是在域转移下.
- 这些方法使得可靠的标记不可靠的预测,没有基础真相注释.
- 改进的模型校准支持在异质的临床环境中更安全地部署深度学习.
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