SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images
This addresses a need in medical imaging for better disease treatment guidance, but appears incremental as it builds on existing GAN and transformer methods.
The paper tackled the problem of generating virtual histological information from coronary OCT images to guide coronary artery disease treatment, proposing SCPAT-GAN to generate H&E histology with improved mapping of pathological regions.
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.