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Survival Analysis

Survival Analysis is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.

Source: Gaussian Processes for Survival Analysis

Image: Kvamme et al.

Papers

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Showing 1–50 of 472 papers

TitleStatusHype
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational PathologyCode2
Neural interval-censored survival regression with feature selectionCode2
FastCPH: Efficient Survival Analysis for Neural NetworksCode2
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
TorchSurv: A Lightweight Package for Deep Survival AnalysisCode2
forester: A Tree-Based AutoML Tool in RCode2
Optimal Survival Trees: A Dynamic Programming ApproachCode1
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological ImagesCode1
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AICode1
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality RebalanceCode1
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual RepresentationCode1
A General Framework for Survival Analysis and Multi-State ModellingCode1
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric CancerCode1
Deep Cox Mixtures for Survival RegressionCode1
Deep Learning for Survival Analysis: A ReviewCode1
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
A Deep Variational Approach to Clustering Survival DataCode1
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and RetentionCode1
Adversarial Time-to-Event ModelingCode1
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival PredictionCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Cross-Modal Translation and Alignment for Survival AnalysisCode1
DeepHazard: neural network for time-varying risksCode1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
CenTime: Event-Conditional Modelling of Censoring in Survival AnalysisCode1
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability GuaranteesCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
A Deep Recurrent Survival Model for Unbiased RankingCode1
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
HEALNet: Multimodal Fusion for Heterogeneous Biomedical DataCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event OutcomesCode1
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
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