Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection

Abstract

Alzheimer's disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net. This approach incorporates the recently developed vector-scaling (VS) loss into a machine learning pipeline named STREAMLINE. Moreover, it employs Bayesian optimization for hyperparameter learning of both the model and loss function. VS-Opt-Net not only amplifies the contribution of minority examples in proportion to the imbalance level but also addresses the challenge of generalization in training deep networks. In our empirical study, we use MRI-based brain regional measurements as features to conduct the CN vs MCI and AD vs MCI binary classifications. We compare the balanced accuracy of our model with other machine learning models and deep neural network loss functions that also employ class-balanced strategies. Our findings demonstrate that after hyperparameter optimization, the deep neural network using the VS loss function substantially improves balanced accuracy. It also surpasses other models in performance on the AD dataset. Moreover, our feature importance analysis highlights VS-Opt-Net's ability to elucidate biomarker differences across dementia stages.

Keywords: Alzheimer’s Disease; Class-Balanced Deep Learning; Hyperparameter Optimization; Mild Cognitive Impairment; Neuroimaging.

Fig. 1.

VS-Opt-Net integrates the VS loss [10] into the STREAMLINE [23] pipeline and employs Bayesian optimization to adaptively learn hyperparameters for both the model and loss function. In Step 3, the VS enlarges the margin of the minority class (m₁) relative to the majority class’s margin (m₂).

Fig. 2.

SHAP feature importance for DNNs with cross-entropy loss (a,c) and VS-Opt-Net (b,d). (a-b) Top regions for CN vs MCI classification. (c-d) Top regions for AD vs MCI classification. Each figure displays the top 10 features for each case.

Fig. 3.

Brain visualization of the leading 40 features for VS-Opt-Net. Colormap indicates SHAP feature importance; darker shades signify higher significance. Panels (a-d) reveal top features for CN vs MCI classification, while panels (e-h) showcase prime features for AD vs MCI classification. Notably, (a-c) and (e-g) spotlight regions of heightened importance in terms of volume, thickness, and surface area measures for both prediction categories. Panel (d) consolidates (a-c), while panel (h) amalgamates (e-g), displaying the highest importance value when a region encompasses multiple measurements.