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AI-based Multiplex Image Analysis of Pathology Slides

AI-based universal and quantitative automation of single-cell detection from multiplex pathology slides with minimal human input

Background

Recent advances in single-cell sequencing are revealing the complexity of heterogeneous cell populations within the tumour microenvironment. The abundance and spatial location of subsets of cells have been linked with tumour behaviour and the response to therapies, highlighting the need to carry out detailed mapping of single cells within the tumour microenvironment to improve understanding of their roles during disease progression.

The development of multiplexed imaging techniques, such as multiplexed immunohistochemistry (mIHC), multiplexed immunofluorescence (mIF) and imaging mass spectrometry (IMC) have enabled researchers to map the spatial distribution of proteins and transcripts within tumour samples.

A common task following multiplexed imaging is to identify specific cell types marked by antibody staining. However, the manual annotation of individual cells by

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