Simple Summary In this study, we propose a general artificial intelligence (AI) based
workflow for applying deep learning to the problem of cell identification in
immunohistochemistry-stained slides as a basis for quantifying nuclear staining biomarkers.
Our approach consists of two main parts: a simplified but robust annotation process, and the
application of cell identification models. This results in an optimised process with a new user-
friendly tool that can interact with other open-source software and assists pathologists and …

Biomarkers identify patient response to therapy. The potential immune-checkpoint
biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation
and involved in adaptive immune responses, is of great interest. We have previously shown
that opensource software for digital pathology image analysis can be used to detect and
quantify ICOS using cell detection algorithms based on traditional image processing
techniques. Currently, artificial intelligence (AI) based on deep learning methods is …