Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

Abstract Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneou...

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Bibliographic Details
Main Authors: Xuewei Wu, Shuaitong Zhang, Zhenyu Zhang, Zicong He, Zexin Xu, Weiwei Wang, Zhe Jin, Jingjing You, Yang Guo, Lu Zhang, Wenhui Huang, Fei Wang, Xianzhi Liu, Dongming Yan, Jingliang Cheng, Jing Yan, Shuixing Zhang, Bin Zhang
Format: Article
Language:English
Published: Nature Portfolio 2024-08-01
Series:npj Precision Oncology
Online Access:https://doi.org/10.1038/s41698-024-00670-2

Internet

https://doi.org/10.1038/s41698-024-00670-2

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