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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

Showing 110 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
forester: A Tree-Based AutoML Tool in RCode2
TorchSurv: A Lightweight Package for Deep Survival AnalysisCode2
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
FastCPH: Efficient Survival Analysis for Neural NetworksCode2
Neural interval-censored survival regression with feature selectionCode2
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality RebalanceCode1
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual RepresentationCode1
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
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