SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival

Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients’ quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive...

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Bibliographic Details
Published in:	Computers in biology and medicine Vol. 172; p. 108301
Main Authors:	Pan, Liangrui, Peng, Yijun, Li, Yan, Wang, Xiang, Liu, Wenjuan, Xu, Liwen, Liang, Qingchun, Peng, Shaoliang
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.04.2024 Elsevier Limited
Subjects:	Cancer Coders Convolutional mask encoder Embedding Graph theory Heterogeneous graph Humans Internal Medicine Missing Modules Multimodal Neoplasms - diagnostic imaging Other Patients Predictions Quality of Life Reconstruction Robust Robustness Survival Missing Heterogeneous graph Multimodal Robust Convolutional mask encoder
ISSN:	0010-4825, 1879-0534, 1879-0534
Online Access:	Get full text
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