  Despite their success in many computer vision tasks, convolutional networks
tend to require large amounts of labeled data to achieve generalization.
Furthermore, the performance is not guaranteed on a sample from an unseen
domain at test time, if the network was not exposed to similar samples from
that domain at training time. This hinders the adoption of these techniques in
clinical setting where the imaging data is scarce, and where the intra- and
inter-domain variance of the data can be substantial. We propose a domain
adaptation technique that is especially suitable for deep networks to alleviate
this requirement of labeled data. Our method utilizes gradient reversal layers
and Squeezeand-Excite modules to stabilize the training in deep networks. The
proposed method was applied to publicly available histopathology and chest
X-ray databases and achieved superior performance to existing state-of-the-art
networks with and without domain adaptation. Depending on the application, our
method can improve multi-class classification accuracy by 5-20% compared to
DANN introduced in (Ganin, 2014).
