Cost-Sensitive Meta-learning for Progress Prediction of Subjective Cognitive Decline with Brain Structural MRI

Subjective cognitive decline (SCD) is a preclinical phase of Alzheimer's disease (AD) which occurs before the deficits could be detected by cognitive tests. It is highly desired to predict the progress of SCD for possible intervention of AD-related cognitive decline. Many neuroimaging-based methods have been developed for AD diagnosis, but there are few studies devoted to automated progress prediction of SCD due to the limited number of SCD subjects. Even though some studies proposed to transfer models (trained on AD/MCI) to SCD analysis, the significant domain shift between their data distributions may degrade the prediction performance. To this end, this paper tackles the problem of learning a model from the source data for which can directly generalize to an unseen target domain for SCD prediction. We propose a cost-sensitive meta-learning scheme to simultaneously improve the model generalization and its sensitivity in MRI-based SCD detection. During training, the source domain is divided into virtual meta-train and meta-test sets to explicitly simulate the scenario for early-stage detection of AD. Considering the importance of sensitivity for progressive status detection, we further introduce cost-sensitive learning to enhance the meta-optimization process by encouraging the model to gain higher sensitivity for SCD detection with simulated domain shift. Experiments conducted on the large-scale ADNI dataset and a small-scale SCD dataset have demonstrated the effectiveness of the proposed method.