辅助循环GAN引导的从双联到单联IHC图像的任务感知域转换
Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images
AI总结：
针对双联IHC到单联IHC图像转换中循环一致性约束失效的问题，提出利用免疫荧光图像作为辅助域的新型训练约束，并在下游分割任务中验证了其优于基线方法。
摘要：
生成模型能够实现从已有现成训练模型的源图像域到训练期间未见过的目标域的转换。虽然循环生成对抗网络（GANs）已经相当成熟，但与之相关的循环一致性约束依赖于两个域之间存在可逆映射。然而，对于使用显色单联和双联免疫组织化学（IHC）染色法染色的图像之间的转换而言，情况并非如此。针对从后者到前者的转换，我们通过引入一种新颖的训练设计，提出了一种替代约束，利用一组免疫荧光（IF）图像作为辅助的非配对图像域。在下游分割任务上的定量和定性结果表明，与基线方法相比，所提出的方法具有优势。
英文摘要：
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.
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。
