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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 101–125 of 472 papers

TitleStatusHype
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce—0
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive ModellingCode0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations—0
Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU NetworksCode0
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression AnalysisCode0
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological ImagesCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal LearningCode0
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes—0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba—0
Conditioning on Time is All You Need for Synthetic Survival Data GenerationCode0
Clustering Survival Data using a Mixture of Non-parametric ExpertsCode0
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis—0
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
Conformalized Survival Distributions: A Generic Post-Process to Increase CalibrationCode0
ResSurv: Cancer Survival Analysis Prediction Model Based on Residual Networks—0
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