ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation
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.
- Domain adaptation-based augmentation for weakly supervised nuclei detection. COMPAY workshop - arXiv preprint arXiv:1907.04681, 2019.
- Stain isolation-based guidance for improved stain translation. MIDL short paper - arXiv preprint arXiv:2207.00431, 2022.
- Generative adversarial networks in digital pathology and histopathological image processing: A review. Journal of Pathology Informatics, 12(1):43, 2021.
- Bayesian k-svd for h and e blind color deconvolution. applications to stain normalization, data augmentation and cancer classification. Computerized Medical Imaging and Graphics, 97:102048, 2022.
- Staingan: Stain style transfer for digital histological images. In ISBI, pages 953–956. IEEE, 2019.
- scikit-image: image processing in python. PeerJ, 2:e453, 2014.
- Structure-preserving multi-domain stain color augmentation using style-transfer with disentangled representations. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VIII 24, pages 257–266. Springer, 2021.
- Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 3666–3673, 2020.
- Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223–2232, 2017.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.