  FDG-PET reveals altered brain metabolism in individuals with mild cognitive
impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from
FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that
they can accurately diagnosis normal control (NC), MCI, and AD. However, the
studies of identification of early MCI (EMCI) and late MCI (LMCI) with FDG-PET
images are still insufficient. Compared with studies based on fMRI and DTI
images, the researches of the inter-region representation features in FDG-PET
images are insufficient. Moreover, considering the variability in different
individuals, some hard samples which are very similar with both two classes
limit the classification performance. To tackle these problems, in this paper,
we propose a novel bilinear pooling and metric learning network (BMNet), which
can extract the inter-region representation features and distinguish hard
samples by constructing embedding space. To validate the proposed method, we
collect 998 FDG-PET images from ADNI. Following the common preprocessing steps,
90 features are extracted from each FDG-PET image according to the automatic
anatomical landmark (AAL) template and then sent into the proposed network.
Extensive 5-fold cross-validation experiments are performed for multiple
two-class classifications. Experiments show that most metrics are improved
after adding the bilinear pooling module and metric losses to the Baseline
model respectively. Specifically, in the classification task between EMCI and
LMCI, the specificity improves 6.38% after adding the triple metric loss, and
the negative predictive value (NPV) improves 3.45% after using the bilinear
pooling module.
