Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914777473744896 |
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| author | Han, Minghao Zhang, Xukun Yang, Dingkang Liu, Tao Kuang, Haopeng Feng, Jinghui Zhang, Lihua |
| author_facet | Han, Minghao Zhang, Xukun Yang, Dingkang Liu, Tao Kuang, Haopeng Feng, Jinghui Zhang, Lihua |
| contents | Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt multiple instance learning or graph neural networks under weak supervision. Most of them are unable to uncover the diverse interactions between different types of biological entities(\textit{e.g.}, cell cluster and tissue block) across multiple scales, while such interactions are crucial for patient survival prediction. In light of this, we propose a novel multi-scale heterogeneity-aware hypergraph representation framework. Specifically, our framework first constructs a multi-scale heterogeneity-aware hypergraph and assigns each node with its biological entity type. It then mines diverse interactions between nodes on the graph structure to obtain a global representation. Experimental results demonstrate that our method outperforms state-of-the-art approaches on three benchmark datasets. Code is publicly available at \href{https://github.com/Hanminghao/H2GT}{https://github.com/Hanminghao/H2GT}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19334 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images Han, Minghao Zhang, Xukun Yang, Dingkang Liu, Tao Kuang, Haopeng Feng, Jinghui Zhang, Lihua Computer Vision and Pattern Recognition Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt multiple instance learning or graph neural networks under weak supervision. Most of them are unable to uncover the diverse interactions between different types of biological entities(\textit{e.g.}, cell cluster and tissue block) across multiple scales, while such interactions are crucial for patient survival prediction. In light of this, we propose a novel multi-scale heterogeneity-aware hypergraph representation framework. Specifically, our framework first constructs a multi-scale heterogeneity-aware hypergraph and assigns each node with its biological entity type. It then mines diverse interactions between nodes on the graph structure to obtain a global representation. Experimental results demonstrate that our method outperforms state-of-the-art approaches on three benchmark datasets. Code is publicly available at \href{https://github.com/Hanminghao/H2GT}{https://github.com/Hanminghao/H2GT}. |
| title | Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.19334 |