  Domain adaptation (DA) is an important technique for modern machine
learning-based medical data analysis, which aims at reducing distribution
differences between different medical datasets. A proper domain adaptation
method can significantly enhance the statistical power by pooling data acquired
from multiple sites/centers. To this end, we have developed the Domain
Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source
software package designed for fast facilitation and easy customization of
domain adaptation methods for medical data analysis. The DomainATM is
implemented in MATLAB with a user-friendly graphical interface, and it consists
of a collection of popular data adaptation algorithms that have been
extensively applied to medical image analysis and computer vision. With
DomainATM, researchers are able to facilitate fast feature-level and
image-level adaptation, visualization and performance evaluation of different
adaptation methods for medical data analysis. More importantly, the DomainATM
enables the users to develop and test their own adaptation methods through
scripting, greatly enhancing its utility and extensibility. An overview
characteristic and usage of DomainATM is presented and illustrated with three
example experiments, demonstrating its effectiveness, simplicity, and
flexibility. The software, source code, and manual are available online.
