Automated disease detection in neuroimaging holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contains technical and demographic confounding factors that cause data to both differ between sites and be systematically associated with the disease of interest, thus negatively affecting the robustness of diagnostic models. There is a critical need for diagnostic deep learning models that can train on such imbalanced datasets without being influenced by these confounds. 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 data. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in the 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) and for data from other hospitals. MUCRAN offers a generalizable approach for heterogenous clinical data for deep-learning-based automatic disease detection.
翻译:神经成像中的自动疾病检测有希望提高放射学家的诊断能力,但定期收集的临床数据经常包含技术和人口因素,导致不同地点的数据不同,并系统地与有关疾病相关,从而对诊断模型的稳健性产生消极影响。迫切需要诊断性深层次学习模型,在不受到这些混乱影响的情况下,对此类不平衡的数据集进行培训。在这项工作中,我们还采用了一种新的深层次学习结构,即MUCRAN(多发倒退反反转网络),对临床脑MRI进行深层次学习模型的培训,同时对人口和技术融合因素进行倒退。我们用2019年前从麻省总医院收集的17,076个临床T1轴心脑MINMTIS对MCRAN进行了培训,并表明MUCRAN可以成功地在不受到这些混乱影响的情况下对庞大的临床数据组进行重大纠结。我们还采用了一种方法来量化基于这些模型的不确定性,以便自动检测中自动排除传播数据。我们通过将MCRAN和不确定性量化方法对MERAN进行了整合,为新收集的GHAS的普通数据的精确性和重要数据提供了AMUR。