Can I read A Novel Generative Artificial Intelligence Method for Interference Study on Multiplex Brightfield Immunohistochemistry Images on EtoBox?

A Novel Generative Artificial Intelligence Method for Interference Study on Multiplex Brightfield Immunohistochemistry Images by Mukherjee, Satarupa; Martin, Jim; Nie, Yao is a scholarly article available to read on EtoBox.

What is A Novel Generative Artificial Intelligence Method for Interference Study on Multiplex Brightfield Immunohistochemistry Images about?

Multiplex brightfield imaging offers the advantage of simultaneously analyzing multiple biomarkers on a single slide, as opposed to single biomarker labeling on multiple consecutive slides. To accurately analyze multiple biomarkers localized at the same cellular compartment, two representative biomarker sets were selected as assay models - cMET-PDL1-EGFR and CD8-LAG3-PDL1, where all three biomarkers can co-localize on the cell membrane. One of the most crucial preliminary stages for analyzing such assay is identifying each unique chromogen on individual cells. This is a challenging problem due to the co-localization of membrane stains from all the three biomarkers. It requires advanced color unmixing for creating the equivalent singleplex images from each triplex image for each biomarker. In this project, we developed a cycle-Generative Adversarial Network (cycle-GAN) method for unmixing the triplex images generated from the above-mentioned assays. Three different models were designed to generate the singleplex image for each of the three stains Tamra (purple), QM-Dabsyl (yellow) and Green. A notable novelty of our approach was that the input to the network were images in the optic

Author

Mukherjee, Satarupa; Martin, Jim; Nie, Yao

Published

2024

Language

EN