AV-GAN: Attention-Based Varifocal Generative Adversarial Network for
  Uneven Medical Image Translation
Zexin Li Yiyang Lin Zijie Fang Shuyan Li Xiu Li
Different types of staining highlight different structures in organs, thereby
assisting in diagnosis. However, due to the impossibility of repeated staining,
we cannot obtain different types of stained slides of the same tissue area.
Translating the slide that is easy to obtain (e.g., H&E) to slides of staining
types difficult to obtain (e.g., MT, PAS) is a promising way to solve this
problem. However, some regions are closely connected to other regions, and to
maintain this connection, they often have complex structures and are difficult
to translate, which may lead to wrong translations. In this paper, we propose
the Attention-Based Varifocal Generative Adversarial Network (AV-GAN), which
solves multiple problems in pathologic image translation tasks, such as uneven
translation difficulty in different regions, mutual interference of multiple
resolution information, and nuclear deformation. Specifically, we develop an
Attention-Based Key Region Selection Module, which can attend to regions with
higher translation difficulty. We then develop a Varifocal Module to translate
these regions at multiple resolutions. Experimental results show that our
proposed AV-GAN outperforms existing image translation methods with two virtual
kidney tissue staining tasks and improves FID values by 15.9 and 4.16
respectively in the H&E-MT and H&E-PAS tasks.