中文版 | English
Title

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Author
Corresponding AuthorQi,Jiping; Carin,Lawrence; Chen,Yupeng; Zhao,Shiguang; Gao,Xin
Publication Years
2023-01-20
DOI
Source Title
EISSN
2589-0042
Volume26Issue:1
Abstract
Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists’ annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) – based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
Corresponding
Scopus EID
2-s2.0-85146042593
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524221
DepartmentSUSTech Institute of Microelectronics
Affiliation
1.Computer Science Program,Computer,Electrical and Mathematical Sciences and Engineering (CEMSE) Division,King Abdullah University of Science and Technology (KAUST),Thuwal,23955-6900,Saudi Arabia
2.KAUST Computational Bioscience Research Center (CBRC),King Abdullah University of Science and Technology (KAUST),Thuwal,23955-6900,Saudi Arabia
3.Department of Pathology,The First Affiliated Hospital of Harbin Medical University,Nangang District,23 Youzheng Street, Harbin,150001,China
4.Department of Neurosurgery,The First Affiliated Hospital of Harbin Medical University,Harbin,Heilongjiang Province,150001,China
5.Suffolk University,Boston,United States
6.Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology,Monash University,Melbourne,3800,Australia
7.Monash Data Futures Institute,Monash University,Melbourne,3800,Australia
8.School of Microelectronics,Southern University of Science and Technology,Shenzhen,518055,China
9.Department of Neurosurgery,Shenzhen University General Hospital,Shenzhen,Guangdong Province,518100,China
Corresponding Author AffilicationSUSTech Institute of Microelectronics
Recommended Citation
GB/T 7714
Li,Zhongxiao,Cong,Yuwei,Chen,Xin,et al. Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors[J]. iScience,2023,26(1).
APA
Li,Zhongxiao.,Cong,Yuwei.,Chen,Xin.,Qi,Jiping.,Sun,Jingxian.,...&Gao,Xin.(2023).Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors.iScience,26(1).
MLA
Li,Zhongxiao,et al."Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors".iScience 26.1(2023).
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