This paper presents a survival rank-n-contrast (SurvRNC) method, which incorporates a regularizer into the loss function. This regularizer is designed based on the Rank-N-Contrast loss function to ensure the ordinality among features, thereby enhancing the performance of survival prediction.
Mar 15, 2024 · In this study, we propose Survival Rank-N Contrast (SurvRNC) method, which introduces a loss function as a regularizer to obtain an ordered representation ...
Learning Ordered Representations for Survival Prediction Using Rank-N ...
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Oct 4, 2024 · In this study, we propose Survival Rank-N-Contrast (SurvRNC) method, which introduces a loss function as a regularizer to obtain an ordered representation ...
Oct 6, 2024 · Objective: To review the effectiveness and limitations of different survival prediction tools, report our single center experience of using ESAS ...
SurvRNC is a project focused on survival analysis using Rank-N-Contrast loss to order the latent representation for prognosis.
Oct 24, 2024 · Saeed and his colleagues call their innovation survival rank-n-contrast (SurvRNC) and it is designed to predict survival times for head and ...
Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by ...
In this paper, we propose SurvCORN, a novel method utilizing conditional ordinal ranking networks to predict survival curves directly. Additionally, we ...
SurvCORN: Survival Analysis with Conditional Ordinal Ranking ... SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast.
Rank-N-Contrast (RNC) is proposed, a framework that learns continuous representations for regression by contrasting samples against each other based on their ...