Histopathology analysis relies on Hematoxylin and Eosin (H&E) staining, but fluorescence microscopy offers complementary information. Converting fluorescence images to H&E-like appearance can aid interpretation and integration with standard workflows. We present a Cycle-Consistent Adversarial Network (CycleGAN) approach for unpaired image-to-image translation from multi-channel fluorescence microscopy to pseudo H&E stained histopathology images. The method combines C01 and C02 fluorescence channels into RGB and learns a bidirectional mapping between fluorescence and H&E domains without paired training data. The architecture uses ResNet-based generators with residual blocks and PatchGAN discriminators, trained with adversarial, cycle-consistency, and identity losses. Experiments on fluorescence microscopy datasets show the model generates realistic pseudo H&E images that preserve morphological structures while adopting H&E-like color characteristics. This enables visualization of fluorescence data in a format familiar to pathologists and supports integration with existing H&E-based analysis pipelines.

翻译：组织病理学分析依赖于苏木精-伊红（H&E）染色，但荧光显微镜能提供互补信息。将荧光图像转换为类H&E外观有助于图像解读以及与标准工作流程的整合。本文提出一种循环一致性对抗网络（CycleGAN）方法，用于从多通道荧光显微镜图像到伪H&E染色组织病理学图像的非配对图像转换。该方法将C01和C02荧光通道合并为RGB格式，并在无配对训练数据的情况下学习荧光域与H&E域之间的双向映射。网络架构采用基于ResNet的残差块生成器和PatchGAN判别器，通过对抗损失、循环一致性损失和恒等损失进行训练。在荧光显微镜数据集上的实验表明，该模型能生成逼真的伪H&E图像，在保持形态学结构的同时呈现类H&E染色色彩特征。这使荧光数据能以病理学家熟悉的格式可视化，并支持与现有基于H&E的分析流程进行整合。