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.

翻译：计算机模拟数据集的创建能够将现有标注的适用范围扩展到计算病理学中具有不同染色模式的新领域。因此，该方法有望显著降低训练监督式深度学习模型所需构建大规模像素级精确数据集的相关成本。我们提出一种新颖方法，通过将形态学特异性IHC染色解耦为免疫荧光（IF）图像中的独立图像通道，从而生成计算机模拟免疫组织化学（IHC）图像。通过在所生成的计算机模拟数据集上训练细胞核分割模型，所提方法在定性和定量层面均优于基线方法。