{"id":19179723,"url":"https://github.com/robi56/survival-analysis-using-deep-learning","last_synced_at":"2026-02-17T09:44:25.203Z","repository":{"id":54428772,"uuid":"120414916","full_name":"robi56/Survival-Analysis-using-Deep-Learning","owner":"robi56","description":"This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis","archived":false,"fork":false,"pushed_at":"2023-08-18T18:15:14.000Z","size":14,"stargazers_count":214,"open_issues_count":1,"forks_count":67,"subscribers_count":19,"default_branch":"master","last_synced_at":"2025-01-04T03:14:27.857Z","etag":null,"topics":["bayesian-inference","cancer-research","deep-learning","gaussian-processes","healthcare","machine-learning","survival-analysis","time-series","time-to-event"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/robi56.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2018-02-06T07:00:04.000Z","updated_at":"2024-12-18T15:41:57.000Z","dependencies_parsed_at":"2022-08-13T15:20:38.076Z","dependency_job_id":"f046a6b9-71bf-475f-b8f7-9d917da165ec","html_url":"https://github.com/robi56/Survival-Analysis-using-Deep-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FSurvival-Analysis-using-Deep-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FSurvival-Analysis-using-Deep-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FSurvival-Analysis-using-Deep-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FSurvival-Analysis-using-Deep-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/robi56","download_url":"https://codeload.github.com/robi56/Survival-Analysis-using-Deep-Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240258155,"owners_count":19772969,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","cancer-research","deep-learning","gaussian-processes","healthcare","machine-learning","survival-analysis","time-series","time-to-event"],"created_at":"2024-11-09T10:44:29.816Z","updated_at":"2026-02-17T09:44:25.120Z","avatar_url":"https://github.com/robi56.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"#  Survial Analysis using Deep Learning\n\nThis repository contains Bayesian Deep Learning based Articles , Papers and Repositories for Survival Analysis.\n\n## Papers\n1. Deep Survival Analysis by Rajesh Ranganath,Adler Perotte,David Blei et all. JMLR 2016\u003cbr\u003e\nSource: http://proceedings.mlr.press/v56/Ranganath16.pdf \n2. The Survival Filter: Joint Survival Analysis with a Latent Time Series by Rajesh Ranganath,Adler Perotte,David Blei et all. UAI, 2015\u003cbr\u003e\nSource: https://www.cs.princeton.edu/~rajeshr/papers/15uai.pdf\n3. 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 \u003cbr\u003e\nSource: https://arxiv.org/abs/1606.00931\n4. Deep Multi-task Gaussian Processes for\nSurvival Analysis with Competing Risks by Ahmed M. Alaa, Mihaela van der Schaar. NIPS 2017 \u003cbr\u003e\nSource: http://papers.nips.cc/paper/6827-deep-multi-task-gaussian-processes-for-survival-analysis-with-competing-risks.pdf\n5. DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks by Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar. 2018 \u003cbr\u003e\nSource: http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit.pdf \n6.  Deep Learning for Patient-Specific Kidney Graft Survival Analysis by Margaux Luck, Tristan Sylvain, Héloïse Cardinal, Andrea Lodi, Yoshua Bengio. 2017 \u003cbr\u003e\nSource: https://arxiv.org/abs/1705.10245\n7.  WSISA: Making Survival Prediction from Whole Slide Histopathological Images by  Xinliang Zhu, Jiawen Yao, Feiyun Zhu, and Junzhou Huang. CVPR 2017\u003cbr\u003e\nSource: http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhu_WSISA_Making_Survival_CVPR_2017_paper.pdf\n8. Deep Integrative Analysis for Survival Prediction by Chenglong Huang, Albert Zhang and Guanghua Xiao,Pacific Symposium on  Biocomputing  2018 . \u003cbr\u003e\nSource: https://pdfs.semanticscholar.org/3a9d/c97916ed05badf0e4c913bf293cbd9a4d82c.pdf\n9.  Deep Correlational Learning for Survival Prediction from Multi-modality Data  by Jiawen Yao, Xinliang Zhu, Feiyun Zhu, Junzhou Huang.MICCAI 2017 \u003cbr\u003e\nSource: https://link.springer.com/chapter/10.1007/978-3-319-66185-8_46 \n10. Deep convolutional neural network for survival analysis with pathological images by Xinliang Zhu, Jiawen Yao,Junzhou Huang. BIBM 2016  \u003cbr\u003e Source: http://ieeexplore.ieee.org/abstract/document/7822579/\n11. 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. \u003cbr\u003e \nSource: https://www.nature.com/articles/s41598-017-11817-6\n12. Combining Deep Learning and Survival Analysis for Asset Health\nManagement by Linxia Liao, Hyung-il Ahn. International Journal of Prognostics and Health Management, 2016\u003cbr\u003e\nSource: https://pdfs.semanticscholar.org/4974/0c7f9923425c4a2942c7e382beaf78cbd4fe.pdf\n13. A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning by Hongyoon Choi\n,  Kwon Joong Na, BioMed Research International 2018.\u003cbr\u003e\nSource: http://downloads.hindawi.com/journals/bmri/aip/2914280.pdf\n14.  Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework by Stephane Fotso.2018 \u003cbr\u003e\nSource: https://arxiv.org/abs/1801.05512\n15. Scalable Joint Models for Reliable\nUncertainty-Aware Event Prediction by Hossein Soleimani,  James Hensman,  and Suchi Saria. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017 \u003cbr\u003e\nSource: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8013802\n16. 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 \u003cbr\u003e \nSource: https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-017-2775-6 \u003cbr\u003e\n17.Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior by Tamara Fernández, Yee Whye Teh . 2016 \u003cbr\u003e\nSource: https://arxiv.org/abs/1611.02335\n18. 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 \u003cbr\u003e \nSource: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-017-0383-8\n19. Gaussian Processes for Survival Analysis by Tamara Fernandez, Nicolas Rivera, Yee Whye Teh. NIPS 2016 \u003cbr\u003e\nSource: http://papers.nips.cc/paper/6443-gaussian-processes-for-survival-analysis.pdf\n20. 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 \u003cbr\u003e \nSource: http://clincancerres.aacrjournals.org/content/clincanres/early/2017/10/05/1078-0432.CCR-17-0853.full.pdf\n21. Going Deep: The Role of Neural Networks for Renal Survival and Beyond by Amelia J.Averitt, Karthik Natarajan. Kidney International Reports, 2018 \u003cbr\u003e\nSource: https://www.sciencedirect.com/science/article/pii/S2468024917304771\n22. 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\u003cbr\u003e \nSource: https://link.springer.com/chapter/10.1007/978-3-319-46723-8_25\n23. 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 \u0026 Guangtao Zhai. Scientific Reports, Nature 2017\u003cbr\u003e\nSource: https://www.nature.com/articles/s41598-017-10649-8\n24. Neural Survival Recommender. WSDM 2017 \u003cbr\u003e\nSource: https://dl.acm.org/citation.cfm?id=3018719\n25. Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks  by Vijaya B.Kolachalama, Vipul C.Chitalia et all.\u003cbr\u003e Kidney International Reports, 2018\nSource: https://www.sciencedirect.com/science/article/pii/S2468024917304370\n26. Deep Recurrent Survival Analysis by Kan Ren, Jiarui Qin et al. AAAI 2019 \u003cbr\u003e\nSource: https://arxiv.org/abs/1809.02403\nCode: https://github.com/rk2900/drsa\n27. Time-to-event prediction with neural networks and Cox regression by Håvard Kvamme, Ørnulf Borgan, and Ida Scheel. JMLR 2019\u003cbr\u003e\nSource: http://jmlr.org/papers/v20/18-424.html\nCode: https://github.com/havakv/pycox\n28. A scalable discrete-time survival model for neural networks by Michael F. Gensheimer and Balasubramanian Narasimhan. PeerJ 2019\u003cbr\u003e\nSource: https://peerj.com/articles/6257/ Code: https://github.com/MGensheimer/nnet-survival\n29. Continuous and discrete-time survival prediction with neural networks by Håvard Kvamme and Ørnulf Borgan. 2019\u003cbr\u003e\nSource: https://arxiv.org/abs/1910.06724\nCode: https://github.com/havakv/pycox\n30. Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks by Nagpal et. al. 2021\u003cbr\u003e\nSource: https://arxiv.org/abs/2003.01176\nCode: https://autonlab.github.io/DeepSurvivalMachines/\n\n## Thesis \n1. Gaussian Process Based\nApproaches for Survival Analysis , Alan D. Saul, University of Sheffield, UK. 2017\u003cbr\u003e \nSource: http://etheses.whiterose.ac.uk/17946/1/thesis.pdf\n2. WTTE-RNN : Weibull Time To Event Recurrent Neural Network, Egil Martinsson, University of Gothenburg, Sweden 2016\u003cbr\u003e\nSource: http://publications.lib.chalmers.se/records/fulltext/253611/253611.pdf\n\n## Software\n1. DeepSurv: DeepSurv is a deep learning approach to survival analysis \u003cbr\u003e\nSource: https://github.com/jaredleekatzman/DeepSurv\nBlogs\n2. SurvivalNet: Deep learning survival models \u003cbr\u003e\nSource: https://github.com/CancerDataScience/SurvivalNet\n3. Pycox: Survival analysis with PyTorch \u003cbr\u003e\nSource: https://github.com/havakv/pycox\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobi56%2Fsurvival-analysis-using-deep-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frobi56%2Fsurvival-analysis-using-deep-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobi56%2Fsurvival-analysis-using-deep-learning/lists"}