多类细分中的不确定性量化:贝叶斯式和非贝叶斯式方法在临床视角中的比较.
Elisa Scalco1, Silvia Pozzi2, Giovanna Rizzo3
1Institute of Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Milan, Italy.
Medical physics
|May 29, 2024
概括
这项研究比较了四种方法来量化AI驱动的医疗图像细分中的不确定性. 测试时间增长 (TTA) 为癌CT扫描提供了最可靠的不确定性估计.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算病理学计算病理学
背景情况:
- 卷积神经网络 (CNN) 自动化医疗图像细分,减少时间和可变性.
- 临床采用受阻于难以准确量化细分不确定性的挑战.
研究的目的:
- 从两个贝叶斯式和两个非贝叶斯式CNN方法评估不确定性量化.
- 将这些方法的风险倾向与癌 (RC) CT图像细分方法进行比较.
主要方法:
- 实施贝叶斯CNN (BDR,BDC),ensemble (Ens) 和测试时间增强 (TTA) 方法.
- 在Kits21和其他脏CT数据集上比较细分精度 (Dice分数) 和不确定性 (RCC,RIU).
- 对观察者之间的变化进行评估.
主要成果:
- 根据结构的准确性有所不同 (:脏0.92,瘤0.58,囊0.21).
- TTA显示了最高的不确定性; BDR显示最低,但具有更不正确的某些像素.
- BDR是冒险的 (更高的准确性,更少的错误不确定性),而另一些则是保守的 (更多的错误报警).
- TTA显示了与观察者间变异性达成的最高一致性 (Dice = 0.94).
结论:
- 量化细分不确定性对于临床决策至关重要.
- 选择的方法应与应用程序所需的风险倾向和政策保持一致.
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