隐式与显式贝叶斯先验对于临床决策支持中的认识不确定性估计
Malte Blattmann1, Adrian Lindenmeyer1, Stefan Franke1
1Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstraße 14, Leipzig, Germany.
深度学习模型可以帮助个性化医疗,但与不确定性作斗争. 像SNGP这样的显式远程感知贝叶斯深度学习方法,为临床决策支持提供更可靠的不确定性估计.
科学领域:
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 深度学习模型显示出个性化医疗的前景.
- 可靠性问题随着分布外数据和过度自信的预测而出现.
- 量化认识体系的不确定性对于可靠的临床决策支持至关重要.
研究的目的:
- 将近似贝叶斯深度学习方法与不确定性量化方法进行比较.
- 评估模型在预测前列腺癌死亡率方面的性能.
- 确定可靠临床决策支持工具的方法.
主要方法:
- 对前列腺癌死亡率数据 (PLCO试验) 应用了三种近似贝叶斯深度学习方法.
- 将隐式功能先验方法 (NN集,VBNNs) 与显式距离感知先验 (SNGP) 进行比较.
- 评估区分性表现 (AUROC) 和认识不确定性估计的校准.
主要成果:
- 所有方法都取得了强的性能 (AUROC = 0.86),并且在分布中进行了精确校准的概率.
- 隐式功能先验方法显示了降低的忠实度和偏的认识系统不确定性估计.
- 明确的距离感知SNGP模型提供了更准确的后方近似和可靠的不确定性量化.
结论:
- 显式远程感知贝叶斯深度学习架构提供了优越的不确定性量化.
- 这些方法对开发可靠的临床决策支持系统充满希望.
- 准确的不确定性估计是医疗保健中可靠的AI的关键.
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