用变化网络对声速重建进行信任归因的不确定性估计
Sonia Laguna1, Lin Zhang1, Can Deniz Bezek2
1Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland.
概括
音速成像中的不确定性估计有助于选择最佳的超声波框架来诊断乳腺癌. 这种方法提高了区分良性病变和恶性病变的准确性.
科学领域:
- 医学成像医学成像
- 生物医学工程 生物医学工程
- 机器学习是机器学习.
背景情况:
- 声速成像 (SoS) 提供了一个有前途的诊断生物标志物.
- 变异网络是有效的从超声波数据的SoS重建.
- 噪音大的超声波可以降低SoS图像质量和诊断准确度.
研究的目的:
- 开发一种基于不确定性的方法来选择可靠的超声波用于SoS成像.
- 通过追溯选择最可靠的数据采集来改进诊断决策.
- 为了提高基于SoS的乳腺癌诊断的准确性.
主要方法:
- 在SoS重建中使用不确定性估计来评估单个超声波.
- 实现基于不确定性量化的自动框架选择.
- 调查蒙特卡洛脱落和贝叶斯变量推理的不确定性估计.
主要成果:
- 基于不确定性的框架选择方法被评估在BI-RADS 4乳腺病变中区分良性纤维腺瘤和恶性癌症.
- 使用不确定性标准识别的最佳框架将曲线下的面积 (AUC) 提高到76% (蒙特卡洛脱落) 和80% (贝叶斯变量推理).
- 这些结果显著优于不确定性无信息基线,该基线达到64%的最佳AUC.
结论:
- 介绍了一种新的不确定性估计应用,用于在医学成像中选择最佳的数据采集.
- 这种方法提高了SoS成像对临床决策的可靠性.
- 拟议的方法显示了改善乳腺癌评估诊断性能的潜力.
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