Summary of Graph Neural Networks in Histopathology: Emerging Trends and Future Directions, by Siemen Brussee et al.
Graph Neural Networks in Histopathology: Emerging Trends and Future Directions
by Siemen Brussee, Giorgio Buzzanca, Anne M.R. Schrader, Jesper Kers
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Tissues and Organs (q-bio.TO)
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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 This comprehensive review focuses on Graph Neural Networks (GNNs) in histopathology, exploring their applications and emerging trends. GNNs excel at modeling pairwise interactions, discerning topological tissue and cellular structures within Whole Slide Images (WSIs). With the surge in deep learning methods for WSIs, this paper highlights the potential of GNNs in capturing intricate spatial dependencies. The review begins by explaining GNN fundamentals and their applications in histopathology, followed by a quantitative literature analysis identifying four emerging trends: Hierarchical GNNs, Adaptive Graph Structure Learning, Multimodal GNNs, and Higher-order GNNs. The paper concludes with proposed future directions to propel the field forward. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This review looks at how computers can help doctors by using special types of artificial intelligence called Graph Neural Networks (GNNs). These networks are great at analyzing pictures of tissues and cells, which is important for diagnosing diseases. The paper talks about what GNNs do well, such as recognizing patterns in tissue structures, and identifies four new ways that scientists are using GNNs to improve medical analysis. It also suggests what might be next in this field. |
Keywords
» Artificial intelligence » Deep learning » Gnn
