Discover and read the best of Twitter Threads about #cpath
Most recents (2)
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…
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
To formalize this, we first used mutual information to identify groups of genes whose expression in breast cancer patients are significantly inter-dependent and form the basis of important cancer and biological pathways and can be used for precision medicine.
"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
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.
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.
2. We introduce a flexible #GNN based #ML framework (#SlideGraph+) that can holistically model WSIs for different large scale prediction problems
3. For HER2 status prediction, the method gives AUC 0.75-0.8 and can be used for case triaging & advanced ordering of tests.
3. For HER2 status prediction, the method gives AUC 0.75-0.8 and can be used for case triaging & advanced ordering of tests.
