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What is Supervised Information Mining From Weakly Paired Images For Breast IHC Virtual Staining about?

The article discusses a novel approach for virtual staining of breast tissue using a generative adversarial network (SIM-GAN) that incorporates supervised information mining techniques to enhance the accuracy of immunohistochemistry (IHC) staining from Hematoxylin and Eosin (H&E) images. The proposed method addresses challenges related to weakly paired images and tissue misalignment, significantly reducing the time required for IHC staining from 48 hours to approximately 3 minutes. Experimental results demo

Author

Abhishek sharma

Language

EN