ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico
Data Generation
- URL: http://arxiv.org/abs/2403.06545v1
- Date: Mon, 11 Mar 2024 09:45:34 GMT
- Title: ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico
Data Generation
- Authors: Dominik Winter, Nicolas Triltsch, Philipp Plewa, Marco Rosati, Thomas
Padel, Ross Hill, Markus Schick, Nicolas Brieu
- Abstract summary: In-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology.
We propose a novel approach for the generation of in-silicochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images.
- Score: 0.12564343689544843
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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. The proposed approach qualitatively and quantitatively outperforms
baseline methods as proven by training nucleus segmentation models on the
created in-silico datasets.
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