AV-GAN: Attention-Based Varifocal Generative Adversarial Network for Uneven Medical Image Translation
This addresses a domain-specific issue in medical imaging by enabling virtual staining to assist diagnosis, but it is incremental as it builds on existing GAN-based translation methods.
The paper tackles the problem of translating medical images between different staining types, where some regions are difficult to translate due to complex structures, by proposing AV-GAN, which improves FID values by 15.9 and 4.16 in two kidney tissue staining tasks compared to existing methods.
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.