ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation

arXiv:2403.06545v1 Announce Type: cross
Abstract: 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. …

abstract annotations arxiv building computational cost cs.ai cs.cv cs.lg data datasets domain domains eess.iv expand pathology patterns pixel translation type utility