Survial Analysis using Deep Learning

This repository contains Bayesian Deep Learning based Articles , Papers and Repositories for Survival Analysis.

Papers

1. Deep Survival Analysis by Rajesh Ranganath,Adler Perotte,David Blei et all. JMLR 2016
   Source: http://proceedings.mlr.press/v56/Ranganath16.pdf
2. The Survival Filter: Joint Survival Analysis with a Latent Time Series by Rajesh Ranganath,Adler Perotte,David Blei et all. UAI, 2015
   Source: https://www.cs.princeton.edu/~rajeshr/papers/15uai.pdf
3. DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network by Jared Katzman, Uri Shaham, Jonathan Bates, Alexander Cloninger, Tingting Jiang, Yuval Kluger . ACML 2016
   Source: https://arxiv.org/abs/1606.00931
4. Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks by Ahmed M. Alaa, Mihaela van der Schaar. NIPS 2017
   Source: http://papers.nips.cc/paper/6827-deep-multi-task-gaussian-processes-for-survival-analysis-with-competing-risks.pdf
5. DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks by Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar. 2018
   Source: http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit.pdf
6. Deep Learning for Patient-Specific Kidney Graft Survival Analysis by Margaux Luck, Tristan Sylvain, Héloïse Cardinal, Andrea Lodi, Yoshua Bengio. 2017
   Source: https://arxiv.org/abs/1705.10245
7. WSISA: Making Survival Prediction from Whole Slide Histopathological Images by Xinliang Zhu, Jiawen Yao, Feiyun Zhu, and Junzhou Huang. CVPR 2017
   Source: http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhu_WSISA_Making_Survival_CVPR_2017_paper.pdf
8. Deep Integrative Analysis for Survival Prediction by Chenglong Huang, Albert Zhang and Guanghua Xiao,Pacific Symposium on Biocomputing 2018 .
   Source: https://pdfs.semanticscholar.org/3a9d/c97916ed05badf0e4c913bf293cbd9a4d82c.pdf
9. Deep Correlational Learning for Survival Prediction from Multi-modality Data by Jiawen Yao, Xinliang Zhu, Feiyun Zhu, Junzhou Huang.MICCAI 2017
   Source: https://link.springer.com/chapter/10.1007/978-3-319-66185-8_46
10. Deep convolutional neural network for survival analysis with pathological images by Xinliang Zhu, Jiawen Yao,Junzhou Huang. BIBM 2016
    Source: http://ieeexplore.ieee.org/abstract/document/7822579/
11. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models by S Yousefi, F Amrollahi, M Amgad, C Dong, JE Lewis… - Scientific Reports, 2017 - nature.com.
    Source: https://www.nature.com/articles/s41598-017-11817-6
12. Combining Deep Learning and Survival Analysis for Asset Health Management by Linxia Liao, Hyung-il Ahn. International Journal of Prognostics and Health Management, 2016
    Source: https://pdfs.semanticscholar.org/4974/0c7f9923425c4a2942c7e382beaf78cbd4fe.pdf
13. A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning by Hongyoon Choi , Kwon Joong Na, BioMed Research International 2018.
    Source: http://downloads.hindawi.com/journals/bmri/aip/2914280.pdf
14. Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework by Stephane Fotso.2018
    Source: https://arxiv.org/abs/1801.05512
15. Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction by Hossein Soleimani, James Hensman, and Suchi Saria. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017
    Source: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8013802
16. Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption by Justine B. Nasejje, Henry Mwambi. BMC Research Notes 2017
    Source: https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-017-2775-6
    17.Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior by Tamara Fernández, Yee Whye Teh . 2016
    Source: https://arxiv.org/abs/1611.02335
17. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data byJustine B. NasejjeEmail author, Henry Mwambi, Keertan Dheda and Maia Lesosky. BMC Medical Research MethodologyBMC series – open, inclusive and trusted 2017
    Source: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-017-0383-8
18. Gaussian Processes for Survival Analysis by Tamara Fernandez, Nicolas Rivera, Yee Whye Teh. NIPS 2016
    Source: http://papers.nips.cc/paper/6443-gaussian-processes-for-survival-analysis.pdf
19. Deep Learning based multi-omics integration robustly predicts survival in liver cancer by Kumardeep Chaudhary, Olivier B Poirion, Liangqun Lu and Lana X Garmire. Clinical Cancer Research, 2018
    Source: http://clincancerres.aacrjournals.org/content/clincanres/early/2017/10/05/1078-0432.CCR-17-0853.full.pdf
20. Going Deep: The Role of Neural Networks for Renal Survival and Beyond by Amelia J.Averitt, Karthik Natarajan. Kidney International Reports, 2018
    Source: https://www.sciencedirect.com/science/article/pii/S2468024917304771
21. 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients by Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, Dinggang Shen. MICCAI 2016
    Source: https://link.springer.com/chapter/10.1007/978-3-319-46723-8_25
22. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme by Jiangwei Lao, Yinsheng Chen, Zhi-Cheng Li, Qihua Li, Ji Zhang, Jing Liu & Guangtao Zhai. Scientific Reports, Nature 2017
    Source: https://www.nature.com/articles/s41598-017-10649-8
23. Neural Survival Recommender. WSDM 2017
    Source: https://dl.acm.org/citation.cfm?id=3018719
24. Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks by Vijaya B.Kolachalama, Vipul C.Chitalia et all.
    Kidney International Reports, 2018 Source: https://www.sciencedirect.com/science/article/pii/S2468024917304370
25. Deep Recurrent Survival Analysis by Kan Ren, Jiarui Qin et al. AAAI 2019
    Source: https://arxiv.org/abs/1809.02403 Code: https://github.com/rk2900/drsa
26. Time-to-event prediction with neural networks and Cox regression by Håvard Kvamme, Ørnulf Borgan, and Ida Scheel. JMLR 2019
    Source: http://jmlr.org/papers/v20/18-424.html Code: https://github.com/havakv/pycox
27. A scalable discrete-time survival model for neural networks by Michael F. Gensheimer and Balasubramanian Narasimhan. PeerJ 2019
    Source: https://peerj.com/articles/6257/ Code: https://github.com/MGensheimer/nnet-survival
28. Continuous and discrete-time survival prediction with neural networks by Håvard Kvamme and Ørnulf Borgan. 2019
    Source: https://arxiv.org/abs/1910.06724 Code: https://github.com/havakv/pycox
29. Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks by Nagpal et. al. 2021
    Source: https://arxiv.org/abs/2003.01176 Code: https://autonlab.github.io/DeepSurvivalMachines/

Thesis

1. Gaussian Process Based Approaches for Survival Analysis , Alan D. Saul, University of Sheffield, UK. 2017
   Source: http://etheses.whiterose.ac.uk/17946/1/thesis.pdf
2. WTTE-RNN : Weibull Time To Event Recurrent Neural Network, Egil Martinsson, University of Gothenburg, Sweden 2016
   Source: http://publications.lib.chalmers.se/records/fulltext/253611/253611.pdf

Software

1. DeepSurv: DeepSurv is a deep learning approach to survival analysis
   Source: https://github.com/jaredleekatzman/DeepSurv Blogs
2. SurvivalNet: Deep learning survival models
   Source: https://github.com/CancerDataScience/SurvivalNet
3. Pycox: Survival analysis with PyTorch
   Source: https://github.com/havakv/pycox