A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
Not on the map. Why?
Abstract (arXiv)
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 1 Feb 2022.
Builds on
9 of 24 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 881
- 2020Captum: A unified and generic model interpretability library for PyTorchcited by 547
- 2020HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classificationcited by 33
Built on by
Not cited yet.