Is it possible to profile #mesothelioma tumors with computational pathology #cpath?
Our paper in @CellRepMed by Mark Eastwood at @TIAwarwick with Jan Robertus at @imperialcollege and colleagues supported by @asthmalunguk, @CR_UK, @MesobanK and @EPSRC presents a solution.
Interactive visualization:
Paper:
Preprint:
Code and data: mesograph.dcs.warwick.ac.uk cell.com/cell-reports-m… arxiv.org/abs/2302.12653 github.com/measty/MesoGra…
Apr 19, 2023 • 12 tweets • 11 min read
What are the #histopathology image #WSI patterns associated with gene expression in cancer? Can we discover them with #AI and use them for spatial profiling and #precision medicine?
Our preprint explores limitations & possibilities of #cpath for this. biorxiv.org/content/10.110…
Existing methods aim to predict expression of individual genes from #WSIs but ignore any inter-dependencies in expression across genes. We show that it is typically not possible to predict expression of a single gene due to such codependencies as shown in fig for a group of genes
May 22, 2022 • 6 tweets • 4 min read
"Whole slide images are graphs": Our paper on effectiveness of graph modelling of WSIs in #cpath & Graph Neural Networks (#GNN) for receptor status prediction of #breastcancer from routine WSIs is accepted in Medical Image Analysis.
Preperint: arxiv.org/abs/2110.06042
TL;DR 1. Motivation: Whole slide images are big and holistic modelling of interactions between different tissue components using only slide level labels is difficult with conventional "patch-then-aggregate" approaches used for weakly supervised learning in CPath.