Prostate Cancer Histology Synthesis Using StyleGAN Latent Space Annotation

Gagandeep B. Daroach, Savannah R. Duenweg, Michael Brehler, Allison K. Lowman, Kenneth A. Iczkowski, Kenneth M. Jacobsohn, Josiah A. Yoder, Peter S. LaViolette

Abstract: The latent space of a generative adversarial network (GAN) may model pathologically-significant semantics with unsupervised learning. To explore this phenomenon, we trained and tested a StyleGAN2 on a high quality prostate histology dataset covering the prostate cancer (PCa) diagnostic spectrum. Our pathologist annotated synthetic images to identify learned PCa regions in the GAN latent space. New points were drawn from these regions, synthesized into images, and given to a pathologist for annotation. 77% of the new points received the same annotation, and 98% of the latent points received the same or adjacent diagnostic stage annotation. This confirms the GAN network can accurately disentangle and model PCa features without exposure to labels in the training process.

External IDs:dblp:conf/miccai/DaroachDBLIJYL22