Article

SHEST: Single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell type prediction and spatial transcriptomics reconstruction

2025-11-19

Abstract excerpt

A comprehensive understanding of cancer progression requires integrating tissue morphol-ogy with spatial molecular profiles. We present SHEST, a multi-task profiling framework that leverages haematoxylin and eosin morphology to predict cellular composition and re-construct spatial gene expression at single-cell resolution. SHEST employs a quadruple-tile input capturing nuclear and contextual information, combined...

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Literature Corpus work: 69abd7da-f281-5bde-a60c-d8ab218218cc
DOI: 10.1101/2025.11.19.689364

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