计算机科学
人工智能
机器学习
可扩展性
不确定度量化
推论
分割
深度学习
辍学(神经网络)
随机森林
二元分类
图像分割
校准
概率分布
模式识别(心理学)
支持向量机
数学
数据库
统计
作者
Thomas Buddenkotte,L. Escudero,Mireia Crispin‐Ortuzar,Ramona Woitek,Cathal McCague,James D. Brenton,Ozan Öktem,Evis Sala,Leonardo Rundo
标识
DOI:10.1016/j.compbiomed.2023.107096
摘要
Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.
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