SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional
  Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images
Xueshen Li Hongshan Liu Xiaoyu Song Brigitta C. Brott Silvio H. Litovsky Yu Gan
There is a significant need for the generation of virtual histological
information from coronary optical coherence tomography (OCT) images to better
guide the treatment of coronary artery disease. However, existing methods
either require a large pixel-wisely paired training dataset or have limited
capability to map pathological regions. To address these issues, we proposed a
structural constrained, pathology aware, transformer generative adversarial
network, namely SCPAT-GAN, to generate virtual stained H&E histology from OCT
images. The proposed SCPAT-GAN advances existing methods via a novel design to
impose pathological guidance on structural layers using transformer-based
network.