Dynamic Graph Representation with Knowledge-Aware Attention for Histopathology Whole Slide Image Analysis
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations, emphasizing significant instances but struggling to capture the interactions between instanc...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 11323 - 11332 |
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IEEE
16.06.2024
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| ISSN: | 1063-6919 |
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| Abstract | Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations, emphasizing significant instances but struggling to capture the interactions between instances. Additionally, conventional graph representation methods utilize explicit spatial positions to construct topological structures but restrict the flexible interaction capabilities between instances at arbitrary locations, particularly when spatially distant. In response, we propose a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. Specifically, we dynamically construct neighbors and directed edge embeddings based on the head and tail relationships between instances. Then, we devise a knowledge-aware attention mechanism that can update the head node features by learning the joint attention score of each neighbor and edge. Finally, we obtain a graph-level embedding through the global pooling process of the updated head, serving as an implicit representation for the WSI classification. Our end-to-end graph representation learning approach has outperformed the state-of-the-art WSI analysis methods on three TCGA benchmark datasets and in-house test sets. Our code is available at https://github.com/WonderLandxD/WiKG. |
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| AbstractList | Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations, emphasizing significant instances but struggling to capture the interactions between instances. Additionally, conventional graph representation methods utilize explicit spatial positions to construct topological structures but restrict the flexible interaction capabilities between instances at arbitrary locations, particularly when spatially distant. In response, we propose a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. Specifically, we dynamically construct neighbors and directed edge embeddings based on the head and tail relationships between instances. Then, we devise a knowledge-aware attention mechanism that can update the head node features by learning the joint attention score of each neighbor and edge. Finally, we obtain a graph-level embedding through the global pooling process of the updated head, serving as an implicit representation for the WSI classification. Our end-to-end graph representation learning approach has outperformed the state-of-the-art WSI analysis methods on three TCGA benchmark datasets and in-house test sets. Our code is available at https://github.com/WonderLandxD/WiKG. |
| Author | Sun, Qiehe Chen, Yuxuan Guan, Tian Han, Anjia Li, Jiawen He, Yonghong Chu, Hongbo |
| Author_xml | – sequence: 1 givenname: Jiawen surname: Li fullname: Li, Jiawen email: lijiawen21@mails.tsinghua.edu.cn organization: Shenzhen International Graduate School, Tsinghua University – sequence: 2 givenname: Yuxuan surname: Chen fullname: Chen, Yuxuan email: chenyx23@mails.tsinghua.edu.cn organization: Shenzhen International Graduate School, Tsinghua University – sequence: 3 givenname: Hongbo surname: Chu fullname: Chu, Hongbo email: zhu-hb23@mails.tsinghua.edu.cn organization: Shenzhen International Graduate School, Tsinghua University – sequence: 4 givenname: Qiehe surname: Sun fullname: Sun, Qiehe organization: Shenzhen International Graduate School, Tsinghua University – sequence: 5 givenname: Tian surname: Guan fullname: Guan, Tian organization: Shenzhen International Graduate School, Tsinghua University – sequence: 6 givenname: Anjia surname: Han fullname: Han, Anjia email: hananjia@mail.sysu.edu.cn organization: The First Affiliated Hospital of Sun Yat-sen University,Department of Pathology – sequence: 7 givenname: Yonghong surname: He fullname: He, Yonghong email: heyh@sz.tsinghua.edu.cn organization: Shenzhen International Graduate School, Tsinghua University |
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| Snippet | Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches... |
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| SubjectTerms | Computer vision Head Heuristic algorithms Histopathology Image analysis Microscopy Representation learning |
| Title | Dynamic Graph Representation with Knowledge-Aware Attention for Histopathology Whole Slide Image Analysis |
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