Effective Immunohistochemistry Pathology Microscopy Image Generation Using CycleGAN - Xu, Zidui; Li, Xi; Zhu, Xihan; Chen, Luyang; He, Yonghong; Chen, Yupeng (Frontiers in Molecular Biosciences, 2020)
Immunohistochemistry detection technology is able to detect more difficult tumors than regular pathology detection technology only with hematoxylin-eosin stained pathology microscopy images, for example neuroendocrine tumor detection. However, making immunohistochemistry pathology microscopy images costs many time and money. In this paper, we propose an effective immunohistochemistry pathology microscopy image generation method which can generate synthetic immunohistochemistry pathology microscopy images from hematoxylin-eosin stained pathology microscopy images without any annotation. Cycle-GAN is adopted as the basic architecture for the unpaired and unannotated dataset. What’s more, multiple instances learning algorithm and the idea behinds conditional GAN are considered to improve the performance. To our knowledge, this is the first attempt to generate immunohistochemistry pathology microscopy images and our method can achieve good performance which will be very useful for pathologists and patients when applied in clinical practice.