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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3269810
|2 doi
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|a pubmed24n1186.xml
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|a (DE-627)NLM356014568
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|a (NLM)37097802
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Li, Shengrui
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing
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| 264 |
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 04.10.2023
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|a Date Revised 04.10.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a 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
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zhao, Yining
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Ting
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Ji
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Yue
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 9 vom: 25. Sept., Seite 11008-11023
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:9
|g day:25
|g month:09
|g pages:11008-11023
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2023.3269810
|3 Volltext
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|d 45
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|h 11008-11023
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