Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors
Li，Zhongxiao1,2; Cong，Yuwei3; Chen，Xin4; Qi，Jiping3; Sun，Jingxian4; Yan，Tao4; Yang，He4; Liu，Junsi4; Lu，Enzhou4; Wang，Lixiang4; Li，Jiafeng4; Hu，Hong4; Zhang，Cheng5; Yang，Quan4; Yao，Jiawei4; Yao，Penglei4; Jiang，Qiuyi4; Liu，Wenwu4; Song，Jiangning6,7; Carin，Lawrence1; Chen，Yupeng8; Zhao，Shiguang4,9; Gao，Xin1,2
|Corresponding Author||Qi，Jiping; Carin，Lawrence; Chen，Yupeng; Zhao，Shiguang; Gao，Xin|
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.
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||SUSTech Institute of Microelectronics|
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 Affilication||SUSTech Institute of Microelectronics|
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).
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).
Li，Zhongxiao,et al."Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors".iScience 26.1(2023).
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.