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 |
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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 |
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