在基于深度学习的医疗图像细分中,用于可扩展的不确定性定量化的校准集
Thomas Buddenkotte1, Lorena Escudero Sanchez2, Mireia Crispin-Ortuzar3
1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany.
Computers in biology and medicine
|June 11, 2023
概括
本研究引入了一个可扩展的框架,用于医疗图像细分中的不确定性量化. 它通过提供更准确的概率估计,增强主动学习和人机协作来改进经典方法.
科学领域:
- 医学图像分析 医学图像分析
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 不确定性量化对于可靠的自动化图像分析至关重要,尤其是深度学习模型.
- 目前深度学习模型中不确定性量化的方法通常在计算上昂贵,并且无法很好地扩展.
- 现有的方法很难准确地近似复杂,高维度问题的分类概率.
研究的目的:
- 开发一个可扩展和直观的框架,用于医疗图像细分中的不确定性量化.
- 解决深度学习中经典不确定性量化技术的局限性.
- 提供不确定性测量,以近似分类概率,以提高模型的解释性和实用性.
主要方法:
- 提出了一个新的,可扩展的框架,用于医疗图像细分中的不确定性量化.
- 证明了古典方法 (例如,学,组合) 在估计分类概率方面的失败.
- 使用k-fold交叉验证来消除对单独校准数据集的需求.
主要成果:
- 拟议的框架产生不确定性测量,近似的分类概率.
- 经典的不确定性量化方法被证明是不适合准确的概率近似.
- K-fold 交叉验证有效地取代了对持有校准数据的需求.
结论:
- 开发的框架为医学图像细分中的不确定性量化提供了一个可扩展和准确的解决方案.
- 该方法通过生成可靠的伪标签来支持增强的积极学习和人机协作.
- 这项工作提升了深度学习在医学成像分析中的可靠性和适用性.
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