MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images

Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction t...

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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2025; s. 15611 - 15620
Hlavní autoři: Ganguly, Aniruddha, Chatterjee, Debolina, Huang, Wentao, Zhang, Jie, Yurovsky, Alisa, Johnson, Travis Steele, Chen, Chao
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.06.2025
Témata:
Accuracy
cell microscopy
computer vision
Gene expression
Graph neural networks
Histopathology
Image edge detection
Measurement
medical and biological vision
Pattern recognition
Smoothing methods
Spatial resolution
spatial transcriptomics
Transcriptomics
ISSN:1063-6919, 1063-6919
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Shrnutí:Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods fail to fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques across multiple metrics.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR52734.2025.01455