CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction
Wenjie Zhao, Jia Li, Mingrui Liu, Jing Wang, Yunhui Guo

TL;DR
CURE-OOD is a benchmark designed to evaluate out-of-distribution detection methods specifically for cancer survival prediction models affected by imaging covariate shifts.
Contribution
It introduces the first systematic benchmark for OOD detection in survival prediction, addressing a gap in evaluating model reliability under distribution shifts.
Findings
Covariate shifts significantly reduce survival prediction accuracy.
Mainstream OOD detectors often fail in survival prediction scenarios.
HazardDev provides a simple survival-aware baseline for OOD detection.
Abstract
``How long can I live and remain free of cancer?'' is often the first question a patient asks after receiving a cancer diagnosis and treatment. Accurate survival prediction helps alleviate psychological distress and supports risk stratification and personalized treatment planning. Recent survival prediction frameworks have shown strong performance using computed tomography (CT) images. However, variations in imaging acquisition introduce out-of-distribution (OOD) samples caused by covariate shifts that undermine model reliability. Despite this challenge, to our knowledge, no existing benchmark systematically studies OOD detection in cancer survival prediction. To address this gap, we introduce the Cancer sURvival bEnchmark for OOD Detection (CURE-OOD), the first benchmark for systematically evaluating OOD detection in survival prediction under controlled acquisition-induced distribution…
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