  Recently, we have witnessed great progress in the field of medical imaging
classification by adopting deep neural networks. However, the recent advanced
models still require accessing sufficiently large and representative datasets
for training, which is often unfeasible in clinically realistic environments.
When trained on limited datasets, the deep neural network is lack of
generalization capability, as the trained deep neural network on data within a
certain distribution (e.g. the data captured by a certain device vendor or
patient population) may not be able to generalize to the data with another
distribution.
  In this paper, we introduce a simple but effective approach to improve the
generalization capability of deep neural networks in the field of medical
imaging classification. Motivated by the observation that the domain
variability of the medical images is to some extent compact, we propose to
learn a representative feature space through variational encoding with a novel
linear-dependency regularization term to capture the shareable information
among medical data collected from different domains. As a result, the trained
neural network is expected to equip with better generalization capability to
the "unseen" medical data. Experimental results on two challenging medical
imaging classification tasks indicate that our method can achieve better
cross-domain generalization capability compared with state-of-the-art
baselines.
