  Subjective cognitive decline (SCD) is a preclinical stage of Alzheimer's
disease (AD) which occurs even before mild cognitive impairment (MCI).
Progressive SCD will convert to MCI with the potential of further evolving to
AD. Therefore, early identification of progressive SCD with neuroimaging
techniques (e.g., structural MRI) is of great clinical value for early
intervention of AD. However, existing MRI-based machine/deep learning methods
usually suffer the small-sample-size problem which poses a great challenge to
related neuroimaging analysis. The central question we aim to tackle in this
paper is how to leverage related domains (e.g., AD/NC) to assist the
progression prediction of SCD. Meanwhile, we are concerned about which brain
areas are more closely linked to the identification of progressive SCD. To this
end, we propose an attention-guided autoencoder model for efficient
cross-domain adaptation which facilitates the knowledge transfer from AD to
SCD. The proposed model is composed of four key components: 1) a feature
encoding module for learning shared subspace representations of different
domains, 2) an attention module for automatically locating discriminative brain
regions of interest defined in brain atlases, 3) a decoding module for
reconstructing the original input, 4) a classification module for
identification of brain diseases. Through joint training of these four modules,
domain invariant features can be learned. Meanwhile, the brain disease related
regions can be highlighted by the attention mechanism. Extensive experiments on
the publicly available ADNI dataset and a private CLAS dataset have
demonstrated the effectiveness of the proposed method. The proposed model is
straightforward to train and test with only 5-10 seconds on CPUs and is
suitable for medical tasks with small datasets.
