Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification
Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification
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DOI：
10.1109/isbi53787.2023.10230493
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Seung-Bin Baek;Injun Choi;Mustafa Dere;Minjeong Kim;Guorong Wu;Won Hwa Kim
中科院分区：
文献类型：
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作者：
Seung-Bin Baek;Injun Choi;Mustafa Dere;Minjeong Kim;Guorong Wu;Won Hwa Kim
当训练样本有限时，DNN中堆叠过多的层会导致高度欠定的系统，这在医学应用中非常常见。在这方面，我们提出了一个框架，能够得到一个有效的高维空间，合理增加模型的大小。这是通过利用变换（即，卷积），其利用具有协方差结构的尺度空间理论。整个模型与下游分类器（即，完全连接层）来捕获原始数据的最佳多尺度表示，该原始数据对应于对偶空间中的特定任务组件。阿尔茨海默病神经成像倡议（ADNI）研究的神经成像措施的实验表明，我们的模型表现更好，收敛速度比传统的模型，即使模型的大小显着减少。使用多尺度变换上的梯度信息使训练的模型可解释，以描绘大脑中的个性化AD特定区域。
Stacking excessive layers in DNN results in highly underdetermined system when training samples are limited, which is very common in medical applications. In this regard, we present a framework capable of deriving an efficient high-dimensional space with reasonable increase in model size. This is done by utilizing a transform (i.e., convolution) that leverages scale-space theory with covariance structure. The overall model trains on this transform together with a downstream classifier (i.e., Fully Connected layer) to capture the optimal multi-scale representation of the original data which corresponds to task-specific components in a dual space. Experiments on neuroimaging measures from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study show that our model performs better and converges faster than conventional models even when the model size is significantly reduced. The trained model is made interpretable using gradient information over the multi-scale transform to delineate personalized AD-specific regions in the brain.