  Most deep learning models for temporal regression directly output the
estimation based on single input images, ignoring the relationships between
different images. In this paper, we propose deep relation learning for
regression, aiming to learn different relations between a pair of input images.
Four non-linear relations are considered: "cumulative relation", "relative
relation", "maximal relation" and "minimal relation". These four relations are
learned simultaneously from one deep neural network which has two parts:
feature extraction and relation regression. We use an efficient convolutional
neural network to extract deep features from the pair of input images and apply
a Transformer for relation learning. The proposed method is evaluated on a
merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold
cross-validation for the task of brain age estimation. The experimental results
have shown that the proposed method achieved a mean absolute error (MAE) of
2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms
with statistical significance (p$<$0.05) in paired T-test (two-side).
