CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction

• URL: http://arxiv.org/abs/2605.00350v1
• Date: Fri, 01 May 2026 02:17:47 GMT
• Title: CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction
• Abstract summary: Cancer survival prediction helps alleviate psychological distress and supports risk stratification and personalized treatment planning.<n>Recent survival prediction frameworks have shown strong performance using computed tomography (CT) images.<n> variations in imaging acquisition introduce out-of-distribution (OOD) samples.<n>No existing benchmark systematically studies OOD detection in cancer survival prediction.
• Score: 44.08596203250721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 shifts. CURE-OOD defines scanner-parameter-based training, in-distribution (ID), and OOD test splits across four survival prediction tasks. Our experiments show that covariate shifts notably reduce survival prediction performance. It also shows that mainstream classification-oriented OOD detectors can fail in survival prediction. Finally, we include HazardDev as a simple survival-aware reference baseline for OOD detection. CURE-OOD enables systematic analysis of how distribution shifts affect both downstream survival performance and OOD detectability.

Related papers

• ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction [77.70461178466043]
  3D semantic occupancy prediction is central to autonomous driving.<n>Current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs.<n>We present ProOOD, a lightweight, plug-and-play method that couples prototype-guided refinement with training-free OOD scoring.
  arXiv  Detail & Related papers  (2026-04-01T16:11:59Z)
• General OOD Detection via Model-aware and Subspace-aware Variable Priority [1.293838707857305]
  Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its training distribution.<n>We introduce a framework for OOD detection that is simultaneously model aware and subspace aware, and that embeds variable prioritization directly into the detection step.
  arXiv  Detail & Related papers  (2025-12-15T05:55:35Z)
• The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection [75.65876949930258]
  Out-of-distribution (OOD) detection is essential for model trustworthiness. We show that the superior OOD detection performance of state-of-the-art methods is achieved by secretly sacrificing the OOD generalization ability.
  arXiv  Detail & Related papers  (2024-10-12T07:02:04Z)
• SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network [4.772480981435387]
  We propose SurvCORN, a novel method utilizing conditional ordinal ranking networks to predict survival curves directly. We also introduce SurvMAE, a metric designed to evaluate the accuracy of model predictions in estimating time-to-event outcomes.
  arXiv  Detail & Related papers  (2024-09-30T03:01:25Z)
• Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection [5.019613806273252]
  Iterative Deployment Exposure (IDE) is a novel and more realistic setting for out-of-distribution (OOD) detection.<n> CSO uses a new U-OOD scoring function that combines the Mahalanobis distance with a nearest-neighbor approach.<n>We validate our approach on a dedicated benchmark, showing that our method greatly improves upon strong baselines on three medical imaging modalities.
  arXiv  Detail & Related papers  (2024-06-04T13:57:34Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• Supervised Machine Learning for Breast Cancer Risk Factors Analysis and Survival Prediction [0.5249805590164902]
  The choice of the most effective treatment may eventually be influenced by breast cancer survival prediction. In this study, 1904 patient records were utilized to predict a 5-year breast cancer survival using a machine learning approach.
  arXiv  Detail & Related papers  (2023-04-13T12:32:14Z)
• Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need [52.88953913542445]
  We find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD)
  arXiv  Detail & Related papers  (2023-02-06T08:24:41Z)
• Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis [17.398553230843717]
  We assess the capability of various state-of-the-art approaches for confidence-based OOD detection. First, we leverage a computer vision benchmark to reproduce and compare multiple OOD detection methods. We then evaluate their capabilities on the challenging task of disease classification using chest X-rays.
  arXiv  Detail & Related papers  (2021-07-06T12:10:09Z)
• Provably Robust Detection of Out-of-distribution Data (almost) for free [124.14121487542613]
  Deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. In this paper we propose a novel method where from first principles we combine a certifiable OOD detector with a standard classifier into an OOD aware classifier. In this way we achieve the best of two worlds: certifiably adversarially robust OOD detection, even for OOD samples close to the in-distribution, without loss in prediction accuracy and close to state-of-the-art OOD detection performance for non-manipulated OOD data.
  arXiv  Detail & Related papers  (2021-06-08T11:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.