High-Order Correlation-Guided Slide-Level Histology Retrieval with Self-Supervised Hashing
Article
Li, Shengrui, Zhao, Yining, Zhang, Jun, Yu, Ting, Zhang, Ji and Gao, Yue. 2023. "High-Order Correlation-Guided Slide-Level Histology Retrieval with Self-Supervised Hashing." IEEE Transactions on Pattern Analysis and Machine Intelligence. 45 (9), pp. 11008-11023. https://doi.org/10.1109/TPAMI.2023.3269810 
| Article Title | High-Order Correlation-Guided Slide-Level Histology Retrieval with Self-Supervised Hashing | 
|---|---|
| ERA Journal ID | 17880 | 
| Article Category | Article | 
| Authors | Li, Shengrui, Zhao, Yining, Zhang, Jun, Yu, Ting, Zhang, Ji and Gao, Yue | 
| Journal Title | IEEE Transactions on Pattern Analysis and Machine Intelligence | 
| Journal Citation | 45 (9), pp. 11008-11023 | 
| Number of Pages | 16 | 
| Year | 2023 | 
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) | 
| Place of Publication | United States | 
| ISSN | 0162-8828 | 
| 1939-3539 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TPAMI.2023.3269810 | 
| Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/10107814 | 
| Abstract | Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide-level retrieval could be more intuitive and practical in clinical applications, most methods are designed for patch-level retrieval. A few recently unsupervised slide-level methods only focus on integrating patch features directly, without perceiving slide-level information, and thus severely limits the performance of WSI retrieval. To tackle the issue, we propose a High-Order Correlation-Guided Self-Supervised Hashing-Encoding Retrieval (HSHR) method. Specifically, we train an attention-based hash encoder with slide-level representation in a self-supervised manner, enabling it to generate more representative slide-level hash codes of cluster centers and assign weights for each. These optimized and weighted codes are leveraged to establish a similarity-based hypergraph, in which a hypergraph-guided retrieval module is adopted to explore high-order correlations in the multi-pairwise manifold to conduct WSI retrieval. Extensive experiments on multiple TCGA datasets with over 24,000 WSIs spanning 30 cancer subtypes demonstrate that HSHR achieves state-of-the-art performance compared with other unsupervised histology WSI retrieval methods. © 1979-2012 IEEE. | 
| Keywords | computer-aided retrieval; Histopathological Whole Slide Image (WSI); slide-level; high-order correlation | 
| ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified | 
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. | 
| Byline Affiliations | Tsinghua University, China | 
| Tencent AI Lab, China | |
| Zhejiang Lab, China | |
| School of Mathematics, Physics and Computing | 
Permalink - 
https://research.usq.edu.au/item/z262z/high-order-correlation-guided-slide-level-histology-retrieval-with-self-supervised-hashing
- 84total views
- 0total downloads
- 8views this month
- 0downloads this month