ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.
ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.
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DOI：
10.1109/tmi.2022.3221890
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发表时间：
2023-04
影响因子：
10.6
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中科院分区：
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医学成像的一个重要目标是能够准确地检测特定于单个扫描的疾病模式；然而，这在脑成像中受到形状和外观的异质性程度的挑战。基于图像配准的传统方法历来无法检测疾病的不同特征，因为它们利用基于总体的分析，主要适用于研究群体平均效应。因此，在本文中，我们利用生成性深度学习的最新发展来开发一种同时分类或回归和特征属性(FA)的方法。具体地说，我们探索了使用称为ICAM的VAE-GAN(可变自动编码器-一般对抗网络)进行翻译，以显式地将类别相关特征从背景混淆中分离出来，以改善神经表型的可解释性和回归。我们使用开发中的人类连接组项目(DHCP)和英国生物库数据集，验证了我们的方法在阿尔茨海默病神经成像倡议(ADNI)队列的小型智力状态检查(MMSE)认知测试分数预测以及神经发育和神经退化的大脑年龄预测任务中的有效性。我们证明了生成的FA图可以用来解释离群点预测，并证明了回归模块的加入改善了潜在空间的解缠。我们的代码可以在GitHub https://github.com/CherBass/ICAM.上免费获得
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.