  Unsupervised domain adaptation is a type of domain adaptation and exploits
labeled data from the source domain and unlabeled data from the target one. In
the Cross-Modality Domain Adaptation for Medical Image Segmenta-tion challenge
(crossMoDA2022), contrast enhanced T1 MRI volumes for brain are provided as the
source domain data, and high-resolution T2 MRI volumes are provided as the
target domain data. The crossMoDA2022 challenge contains two tasks,
segmentation of vestibular schwannoma (VS) and cochlea, and clas-sification of
VS with Koos grade. In this report, we presented our solution for the
crossMoDA2022 challenge. We employ an image-to-image translation method for
unsupervised domain adaptation and residual U-Net the segmenta-tion task. We
use SVM for the classification task. The experimental results show that the
mean DSC and ASSD are 0.614 and 2.936 for the segmentation task and MA-MAE is
0.84 for the classification task.
