Virtual stain transfer is a promising area of research in Computational
Pathology, which has a great potential to alleviate important limitations when
applying deeplearningbased solutions such as lack of annotations and
sensitivity to a domain shift. However, in the literature, the majority of
virtual staining approaches are trained for a specific staining or stain
combination, and their extension to unseen stainings requires the acquisition
of additional data and training. In this paper, we propose HistoStarGAN, a
unified framework that performs stain transfer between multiple stainings,
stain normalisation and stain invariant segmentation, all in one inference of
the model. We demonstrate the generalisation abilities of the proposed solution
to perform diverse stain transfer and accurate stain invariant segmentation
over numerous unseen stainings, which is the first such demonstration in the
field. Moreover, the pre-trained HistoStar-GAN model can serve as a synthetic
data generator, which paves the way for the use of fully annotated synthetic
image data to improve the training of deep learning-based algorithms. To
illustrate the capabilities of our approach, as well as the potential risks in
the microscopy domain, inspired by applications in natural images, we generated
KidneyArtPathology, a fully annotated artificial image dataset for renal
pathology.

本文介绍了 HistoStarGAN，它是一种用于多种染色方式的虚拟染色方法，可以进行染色归一化和染色不变分割，并证明其具有泛化能力和生成合成图像数据的能力。同时还展示了一种基于肾脏病理图像的人工数据集制作方式，以此展示了这种方法在生物成像领域的潜在应用与风险。