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"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.
