Abstract
This work introduces Ui2i, a novel model for unpaired image-to-image translation, trained on content-wise unpaired datasets to enable style transfer across domains while preserving content. Building on CycleGAN, Ui2i incorporates key modifications to better disentangle content and style features, and preserve content integrity. Specifically, Ui2i employs U-Net-based generators with skip connections to propagate localized shallow features deep into the generator. Ui2i removes feature-based normalization layers from all modules and replaces them with approximate bidirectional spectral normalization -- a parameter-based alternative that enhances training stability. To further support content preservation, channel and spatial attention mechanisms are integrated into the generators. Training is facilitated through image scale augmentation. Evaluation on two biomedical tasks -- domain adaptation for nuclear segmentation in immunohistochemistry (IHC) images and unmixing of biological structures superimposed in single-channel immunofluorescence (IF) images -- demonstrates Ui2i's ability to preserve content fidelity in settings that demand more accurate structural preservation than typical translation tasks. To the best of our knowledge, Ui2i is the first approach capable of separating superimposed signals in IF images using real, unpaired training data.
Abstract (translated)
这项工作介绍了Ui2i，这是一种新颖的模型，用于无配对图像到图像的转换。它在基于内容的无配对数据集上进行训练，旨在跨领域进行风格迁移的同时保持内容不变。Ui2i建立在CycleGAN的基础上，对其进行关键修改以更好地分离内容和风格特征，并保护内容完整性。具体来说，Ui2i采用具有跳跃连接的U-Net生成器，将局部浅层特征深入传播到生成器中。Ui2i从所有模块中移除了基于特征的归一化层，并用近似的双向谱归一化进行替换——这是一种参数化的替代方案，增强了训练稳定性。为了进一步支持内容保持，通道和空间注意机制被整合到生成器中。通过图像尺度增强来促进训练过程。 在两个生物医学任务上的评估展示了Ui2i在要求比常规翻译任务更准确结构保留的情况下仍能保持内容保真度的能力：一个是免疫组化(IHC)图像中的细胞核分割领域的适应性；另一个是单通道免疫荧光(IF)图像中叠加的生物结构的解混。据我们所知，Ui2i是首个能够使用真实无配对训练数据分离IF图像中超叠信号的方法。
URL
https://arxiv.org/abs/2505.20746
