Summary of Spatially Resolved Gene Expression Prediction From Histology Via Multi-view Graph Contrastive Learning with Hsic-bottleneck Regularization, by Changxi Chi et al.
Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization
by Changxi Chi, Hang Shi, Qi Zhu, Daoqiang Zhang, Wei Shao
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ST-GCHB framework addresses the challenges of predicting gene expression on spatial transcriptomics data by leveraging corresponding histopathological images. The existing image-based models treat each spot independently, ignoring spatial dependencies. To overcome this limitation, the framework learns a shared representation that considers spatial relationships and uses HSIC-bottleneck regularization to align paired image and expression representations. By doing so, the model can accurately impute gene expressions for queried imaging spots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ST-GCHB framework is an innovative approach to predicting gene expression on spatial transcriptomics data by using histopathological images. This method learns a shared representation that takes into account spatial relationships between data points. The framework also uses HSIC-bottleneck regularization to align paired image and expression representations, which helps improve the accuracy of the predictions. |
Keywords
* Artificial intelligence * Regularization
