  Domain shift between medical images from multicentres is still an open
question for the community, which degrades the generalization performance of
deep learning models. Generative adversarial network (GAN), which synthesize
plausible images, is one of the potential solutions to address the problem.
However, the existing GAN-based approaches are prone to fail at preserving
image-objects in image-to-image (I2I) translation, which reduces their
practicality on domain adaptation tasks. In this paper, we propose a novel GAN
(namely MI$^2$GAN) to maintain image-contents during cross-domain I2I
translation. Particularly, we disentangle the content features from domain
information for both the source and translated images, and then maximize the
mutual information between the disentangled content features to preserve the
image-objects. The proposed MI$^2$GAN is evaluated on two tasks---polyp
segmentation using colonoscopic images and the segmentation of optic disc and
cup in fundus images. The experimental results demonstrate that the proposed
MI$^2$GAN can not only generate elegant translated images, but also
significantly improve the generalization performance of widely used deep
learning networks (e.g., U-Net).
