An Example for Domain Adaptation Using CycleGAN

TL;DR

This paper demonstrates how CycleGAN can be used for domain adaptation in medical imaging by translating microscopy images into pseudo H&E stained histopathology images without paired data.

Contribution

It presents a specific CycleGAN architecture for unpaired image translation from microscopy to histopathology images, illustrating its application in medical domain adaptation.

Findings

Effective unpaired image translation demonstrated

Potential for improved medical image analysis workflows

CycleGAN successfully generates realistic pseudo H&E images

Abstract

Cycle-Consistent Adversarial Network (CycleGAN) is very promising in domain adaptation. In this report, an example in medical domain will be explained. We present struecture of a CycleGAN model for unpaired image-to-image translation from microscopy to pseudo H\&E stained histopathology images.