SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network
Ridzuan, Muhammad Saeed, Numan Maani, Fadillah Adamsyah Nandakumar, Karthik Yaqub, Mohammad
Ridzuan, Muhammad
Saeed, Numan
Maani, Fadillah Adamsyah
Nandakumar, Karthik
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Survival analysis plays a crucial role in estimating the likelihood of future events for patients by modeling time-to-event data, particularly in healthcare settings where predictions about outcomes such as death and disease recurrence are essential. However, this analysis poses challenges due to the presence of censored data, where time-to-event information is missing for certain data points. Yet, censored data can offer valuable insights, provided we appropriately incorporate the censoring time during modeling. In this paper, we propose SurvCORN, a novel method utilizing conditional ordinal ranking networks to predict survival curves directly. Additionally, we introduce SurvMAE, a metric designed to evaluate the accuracy of model predictions in estimating time-to-event outcomes. Through empirical evaluation on two real-world cancer datasets, we demonstrate SurvCORN’s ability to maintain accurate ordering between patient outcomes while improving individual time-to-event predictions. Our contributions extend recent advancements in ordinal regression to survival analysis, offering valuable insights into accurate prognosis in healthcare settings. Our code is available at https://github.com/BioMedIA-MBZUAI/SurvCORN.
Citation
M. Ridzuan, N. Saeed, F. A. Maani, K. Nandakumar, and M. Yaqub, “SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15199 LNCS, pp. 231–240, 2025, doi: 10.1007/978-3-031-73376-5_22.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
MICCAI Workshop on Cancer Prevention through Early Detection
Keywords
ordinal ranking, prognosis, survival analysis
Subjects
Source
MICCAI Workshop on Cancer Prevention through Early Detection
Publisher
Springer Nature