Vision Transformer-based Weakly-Supervised Histopathological Image Analysis of Primary Brain Tumors

Zhongxiao Li, Yuwei Cong, Xin Chen, Jiping Qi, Jingxian Sun, Tao Yan, He Yang, Junsi Liu, Enzhou Lu, Lixiang Wang, Jiafeng Li, Hong Hu, Cheng Zhang, Quan Yang, Jiawei Yao, Penglei Yao, Qiuyi Jiang, Wenwu Liu, Jiangning Song, Lawrence CarinYupeng Chen, SHIGUANG ZHAO, Xin Gao

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)105872
StatePublished - Dec 24 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-12-26
Acknowledged KAUST grant number(s): BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, REI/1/4742-01-01, URF/1/4098-01-01, URF/1/4352-01-01
Acknowledgements: This work was supported by Office of Research Administration (ORA) at KAUST under award numbers BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, URF/1/4098-01-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/4352-01-01, and REI/1/4742-01-01. Figure 1 was created by Heno Hwang, scientific illustrator at King Abdullah University of Science and Technology (KAUST).


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