用于分割和信号分离的无配对图像到图像翻译
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
AI总结：
Ui2i是一种基于CycleGAN改进的无配对图像翻译模型，通过U-Net、谱归一化和注意力机制保留内容，在IHC分割和IF信号分离任务中验证了其结构保真能力。
摘要：
这项工作介绍了Ui2i，一种用于无配对图像到图像翻译的新颖模型，它在内容上无配对的数据集上进行训练，以实现跨域风格迁移同时保留内容。基于CycleGAN，Ui2i融入了关键修改，以更好地解耦内容和风格特征，并保持内容完整性。具体来说，Ui2i采用基于U-Net的生成器，带有跳跃连接，将局部浅层特征传播到生成器深处。Ui2i从所有模块中移除基于特征的归一化层，并用近似双向谱归一化——一种基于参数的替代方案——来增强训练稳定性。为进一步支持内容保留，生成器中集成了通道和空间注意力机制。训练通过图像尺度增强来促进。在两个生物医学任务上的评估——免疫组织化学（IHC）图像中核分割的域适应，以及单通道免疫荧光（IF）图像中叠加生物结构的分离——证明了Ui2i在需要比典型翻译任务更精确结构保留的设置中保持内容保真度的能力。据我们所知，Ui2i是第一个能够使用真实、无配对训练数据分离IF图像中叠加信号的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
