  Deploying reliable deep learning techniques in interdisciplinary applications
needs learned models to output accurate and (even more importantly) explainable
predictions. Existing approaches typically explicate network outputs in a
post-hoc fashion, under an implicit assumption that faithful explanations come
from accurate predictions/classifications. We have an opposite claim that
explanations boost (or even determine) classification. That is, end-to-end
learning of explanation factors to augment discriminative representation
extraction could be a more intuitive strategy to inversely assure fine-grained
explainability, e.g., in those neuroimaging and neuroscience studies with
high-dimensional data containing noisy, redundant, and task-irrelevant
information. In this paper, we propose such an explainable geometric deep
network dubbed as NeuroExplainer, with applications to uncover altered infant
cortical development patterns associated with preterm birth. Given fundamental
cortical attributes as network input, our NeuroExplainer adopts a hierarchical
attention-decoding framework to learn fine-grained attentions and respective
discriminative representations to accurately recognize preterm infants from
term-born infants at term-equivalent age. NeuroExplainer learns the
hierarchical attention-decoding modules under subject-level weak supervision
coupled with targeted regularizers deduced from domain knowledge regarding
brain development. These prior-guided constraints implicitly maximizes the
explainability metrics (i.e., fidelity, sparsity, and stability) in network
training, driving the learned network to output detailed explanations and
accurate classifications. Experimental results on the public dHCP benchmark
suggest that NeuroExplainer led to quantitatively reliable explanation results
that are qualitatively consistent with representative neuroimaging studies.
