  Deep neural networks (DNN) have been designed to predict the chronological
age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs),
and the predicted brain age could serve as a valuable biomarker for the early
detection of development-related or aging-related disorders. Recent DNN models
for brain age estimations usually rely too much on large sample sizes and
complex network structures for multi-stage feature refinement. However, in
clinical application scenarios, researchers usually cannot obtain thousands or
tens of thousands of MRIs in each data center for thorough training of these
complex models. This paper proposes a simple fully convolutional network
(SFCNeXt) for brain age estimation in small-sized cohorts with biased age
distributions. The SFCNeXt consists of Single Pathway Encoded ConvNeXt (SPEC)
and Hybrid Ranking Loss (HRL), aiming to estimate brain ages in a lightweight
way with a sufficient exploration of MRI, age, and ranking features of each
batch of subjects. Experimental results demonstrate the superiority and
efficiency of our approach.
