Generative models enable the translation from a source image domain where readily trained models are available to a target domain unseen during training. While Cycle Generative Adversarial Networks (GANs) are well established, the associated cycle consistency constrain relies on that an invertible mapping exists between the two domains. This is, however, not the case for the translation between images stained with chromogenic monoplex and duplex immunohistochemistry (IHC) assays. Focusing on the translation from the latter to the first, we propose - through the introduction of a novel training design, an alternative constrain leveraging a set of immunofluorescence (IF) images as an auxiliary unpaired image domain. Quantitative and qualitative results on a downstream segmentation task show the benefit of the proposed method in comparison to baseline approaches.

通过引入新的训练设计和利用免疫荧光图像作为辅助未配对图像域，我们提出了一种替代约束方法，实现了来自染色单型和双型免疫组化学染色的图像到相应图像域之间的转换，并在下游分割任务上定量和定性结果显示了该方法相比基准方法的优势。

辅助CycleGAN引导的从双工到单工IHC图像的任务感知域转换