Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
This work addresses the need for virtual staining to reduce reliance on scarce tissue samples and tedious staining procedures in oncology diagnosis and drug development, but it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of generating virtual immunohistochemistry (IHC) images from real IHC images in digital pathology, using an unsupervised deep learning approach based on CycleGAN, and validates the results by comparing tissue analysis algorithms between virtual and real images.
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.