Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/109169
DC Field | Value | Language |
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dc.contributor | Department of Biomedical Engineering | en_US |
dc.creator | Zhao, W | en_US |
dc.creator | Wang, S | en_US |
dc.creator | Yeung, M | en_US |
dc.creator | Niu, T | en_US |
dc.creator | Yu, L | en_US |
dc.date.accessioned | 2024-09-19T05:23:02Z | - |
dc.date.available | 2024-09-19T05:23:02Z | - |
dc.identifier.isbn | 978-1-57735-880-0 (Online) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/109169 | - |
dc.description | Thirty-Seventh AAAI Conference on Artificial Intelligence, February 7-14, 2023, Washington, D.C., USA | en_US |
dc.language.iso | en | en_US |
dc.publisher | Association for the Advancement of Artificial Intelligence | en_US |
dc.rights | © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. | en_US |
dc.rights | The following publication Zhao, W., Wang, S., Yeung, M., Niu, T., & Yu, L. (2023). MulGT: Multi-Task Graph-Transformer with Task-Aware Knowledge Injection and Domain Knowledge-Driven Pooling for Whole Slide Image Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 37(3), 3606-3614 is available at https://doi.org/10.1609/aaai.v37i3.25471. | en_US |
dc.title | MulGT : multi-task graph-transformer with task-aware knowledge injection and domain knowledge-driven pooling for whole slide image analysis | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 3606 | en_US |
dc.identifier.epage | 3614 | en_US |
dc.identifier.doi | 10.1609/aaai.v37i3.25471 | en_US |
dcterms.abstract | Whole slide image (WSI) has been widely used to assist automated diagnosis under the deep learning fields. However, most previous works only discuss the SINGLE task setting which is not aligned with real clinical setting, where pathologists often conduct multiple diagnosis tasks simultaneously. Also, it is commonly recognized that the multi-task learning paradigm can improve learning efficiency by exploiting commonalities and differences across multiple tasks. To this end, we present a novel multi-task framework (i.e., MulGT) for WSI analysis by the specially designed Graph-Transformer equipped with Task-aware Knowledge Injection and Domain Knowledge-driven Graph Pooling modules. Basically, with the Graph Neural Network and Transformer as the building commons, our framework is able to learn task-agnostic low-level local information as well as task-specific high-level global representation. Considering that different tasks in WSI analysis depend on different features and properties, we also design a novel Task-aware Knowledge Injection module to transfer the task-shared graph embedding into task-specific feature spaces to learn more accurate representation for different tasks. Further, we elaborately design a novel Domain Knowledge-driven Graph Pooling module for each task to improve both the accuracy and robustness of different tasks by leveraging different diagnosis patterns of multiple tasks. We evaluated our method on two public WSI datasets from TCGA projects, i.e., esophageal carcinoma and kidney carcinoma. Experimental results show that our method outperforms single-task counterparts and the state-of-theart methods on both tumor typing and staging tasks. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In B Williams, Y Chen, & J Neville (Eds.), Proceedings of the 37th AAAI Conference on Artificial Intelligence, p. 3606-3614. Washington, DC: Association for the Advancement of Artificial Intelligence, 2023 | en_US |
dcterms.issued | 2023 | - |
dc.relation.ispartofbook | Proceedings of the 37th AAAI Conference on Artificial Intelligence | en_US |
dc.relation.conference | Conference on Artificial Intelligence [AAAI] | en_US |
dc.description.validate | 202409 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a3073a [Non PolyU] | - |
dc.identifier.SubFormID | 49379 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | HKU Seed Fund for Basic Research | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | VoR allowed | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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25471-Article Text-29534-1-2-20230626.pdf | 1.17 MB | Adobe PDF | View/Open |
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