Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment
Peer-Reviewed Publication
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Traditional IHC requires one tissue section per stain, exhibits section-to-section variability, and incurs high costs and laborious staining procedures. While multiplexed immunohistochemistry (mIHC) techniques enable simultaneous staining with multiple antibodies on a single slide, they are more tedious to perform and are currently unavailable in routine pathology laboratories. Here, we present a deep learning-based virtual multiplexed immunostaining framework that simultaneously generates ERG and PanCK, in addition to H&E virtual staining, enabling the accurate localization and interpretation of vascular invasion in thyroid cancers.
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