  With recent advances in supervised machine learning for medical image
analysis applications, the annotated medical image datasets of various domains
are being shared extensively. Given that the annotation labelling requires
medical expertise, such labels should be applied to as many learning tasks as
possible. However, the multi-modal nature of each annotated image renders it
difficult to share the annotation label among diverse tasks. In this work, we
provide an inductive transfer learning (ITL) approach to adopt the annotation
label of the source domain datasets to tasks of the target domain datasets
using Cycle-GAN based unsupervised domain adaptation (UDA). To evaluate the
applicability of the ITL approach, we adopted the brain tissue annotation label
on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the
task of brain tumor segmentation on the target domain dataset of MRI. The
results confirm that the segmentation accuracy of brain tumor segmentation
improved significantly. The proposed ITL approach can make significant
contribution to the field of medical image analysis, as we develop a
fundamental tool to improve and promote various tasks using medical images.
