Survival analysis using deep learning with medical imaging
Author:
Affiliation:
1. Department of Biostatistics, School of Public Health , Brown University , Providence , RI , USA
Abstract
Funder
National Institute of General Medical Sciences
National Cancer Institute
Publisher
Walter de Gruyter GmbH
Subject
Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability
Link
https://www.degruyter.com/document/doi/10.1515/ijb-2022-0113/pdf
Reference38 articles.
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2. Zhao, L. Deep neural networks for predicting restricted mean survival times. Bioinformatics 2020;36:5672–7. https://doi.org/10.1093/bioinformatics/btaa1082.
3. Sun, Y, Chiou, SH, Wang, MC. Roc-guided survival trees and ensembles. Biometrics 2020;76:1177–89. https://doi.org/10.1111/biom.13213.
4. Bou-Hamad, I, Larocque, D, Ben-Ameur, H. A review of survival trees. Stat Surv 2011;5:44–71. https://doi.org/10.1214/09-ss047.
5. Cui, Y, Zhu, R, Kosorok, M. Tree based weighted learning for estimating individualized treatment rules with censored data. Electron J Stat 2017;11:3927. https://doi.org/10.1214/17-ejs1305.
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