An Example for Domain Adaptation Using CycleGAN

2026-01-13

AI Summary

This work proposes an unpaired image translation method based on CycleGAN to address the incompatibility of fluorescence microscopy images with standard histopathological workflows that rely on hematoxylin and eosin (H&E) staining. By fusing two fluorescence channels (C01 and C02) into an RGB input, the model translates multi-channel fluorescence images into virtual H&E-like images exhibiting realistic color characteristics. The architecture employs a ResNet-based generator and a PatchGAN discriminator, trained with a combination of adversarial loss, cycle-consistency loss, and identity loss. The generated images preserve the original tissue morphology while accurately mimicking the chromatic appearance of conventional H&E-stained slides, thereby significantly enhancing compatibility with existing pathological analysis pipelines and facilitating multimodal data integration for clinical applications.

Technology Category

Computer Vision: Generative Adversarial Networks (GANs) for VisionMachine Learning: Multimodal LearningIntelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsWeb Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Light-Sheet Microscopy
• H&E Staining
• Image Translation
• Histopathology
• Fluorescence Microscopy

Innovation

• CycleGAN
• image-to-image translation
• fluorescence microscopy
• virtual H&E
• unpaired learning