  Domain shift is a major problem for deploying deep networks in clinical
practice. Network performance drops significantly with (target) images obtained
differently than its (source) training data. Due to a lack of target label
data, most work has focused on unsupervised domain adaptation (UDA). Current
UDA methods need both source and target data to train models which perform
image translation (harmonization) or learn domain-invariant features. However,
training a model for each target domain is time consuming and computationally
expensive, even infeasible when target domain data are scarce or source data
are unavailable due to data privacy. In this paper, we propose a novel self
domain adapted network (SDA-Net) that can rapidly adapt itself to a single test
subject at the testing stage, without using extra data or training a UDA model.
The SDA-Net consists of three parts: adaptors, task model, and auto-encoders.
The latter two are pre-trained offline on labeled source images. The task model
performs tasks like synthesis, segmentation, or classification, which may
suffer from the domain shift problem. At the testing stage, the adaptors are
trained to transform the input test image and features to reduce the domain
shift as measured by the auto-encoders, and thus perform domain adaptation. We
validated our method on retinal layer segmentation from different OCT scanners
and T1 to T2 synthesis with T1 from different MRI scanners and with different
imaging parameters. Results show that our SDA-Net, with a single test subject
and a short amount of time for self adaptation at the testing stage, can
achieve significant improvements.
