用协方差结构构建多尺度表示，提升阿尔茨海默病分类精度与速度
Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification
- 基于尺度空间理论与协方差结构设计多尺度变换模块
- 在模型缩小情况下仍优于传统方法且收敛更快
- 通过梯度可解释性定位个体化脑区异常特征
深度神经网络在样本有限的医疗场景中易因层数过多导致欠定问题。本文提出一种框架，通过结合尺度空间理论与协方差结构的变换，实现高维空间的有效表征，仅适度增加模型规模。整体模型联合训练该变换与下游分类器（全连接层），以捕获原始数据在双空间中的任务相关多尺度表示。在阿尔茨海默病神经影像计划（ADNI）数据集上的实验表明，所提模型在显著减小模型规模的情况下仍表现更优且收敛更快。通过多尺度变换的梯度信息，模型具备可解释性，可精准定位个体化的阿尔茨海默病特异性脑区。
原文摘要 · Abstract (English)
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.
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