The creation of in-silico datasets can expand the utility of existing
annotations to new domains with different staining patterns in computational
pathology. As such, it has the potential to significantly lower the cost
associated with building large and pixel precise datasets needed to train
supervised deep learning models. We propose a novel approach for the generation
of in-silico immunohistochemistry (IHC) images by disentangling morphology
specific IHC stains into separate image channels in immunofluorescence (IF)
images. The proposed approach qualitatively and quantitatively outperforms
baseline methods as proven by training nucleus segmentation models on the
created in-silico datasets.

通过将形态学特定的免疫组织化学染色分解成免疫荧光（IF）图像中的分离图像通道，我们提出了一种新颖的方法来生成模拟免疫组织化学（IHC）图像，该方法在定性和定量上优于基准方法，并通过在创建的模拟数据集上训练细胞核分割模型得到证明。