Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.
Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.
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DOI：
10.1371/journal.pone.0277572
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发表时间：
2023
期刊：
影响因子：
3.7
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中科院分区：
文献类型：
--
作者：
在这项工作中，我们引入了一种新的深度学习架构MUCRAN（多混淆回归对抗网络），用于在临床脑MRI上训练深度学习模型，同时回归人口统计和技术混淆因素。我们使用2019年之前从马萨诸塞州总医院收集的17，076个临床T1轴向脑MRI训练MUCRAN，并证明MUCRAN可以成功回归大量临床数据集中的主要混杂因素。我们还应用了一种方法来量化这些模型集合中的不确定性，以自动排除AD检测中的分布外数据。通过结合MUCRAN和不确定性量化方法，我们发现新收集的MGH数据（2019年后;使用MUCRAN的准确率为84.6%，未使用MUCRAN的准确率为72.5%）和其他医院的数据（Brigham and Women's Hospital的准确率为90.3%，其他医院的准确率为81.0%）的AD检测准确率一致且显著增加。MUCRAN为异质临床数据中基于深度学习的疾病检测提供了一种通用方法。
In this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN could successfully regress major confounding factors in the vast clinical dataset. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in AD detection. By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019; 84.6% with MUCRAN vs. 72.5% without MUCRAN) and for data from other hospitals (90.3% from Brigham and Women’s Hospital and 81.0% from other hospitals). MUCRAN offers a generalizable approach for deep-learning-based disease detection in heterogenous clinical data.
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