# SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast

arXiv: https://arxiv.org/abs/2403.10603 · HTML: https://mlsanity.com/paper/2403.10603/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework](https://mlsanity.com/paper/1801.05512/) (2018)
- [TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction](https://mlsanity.com/paper/2209.05036/) (2022)
- [An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data](https://mlsanity.com/paper/2202.12537/) (2022)
- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer](https://mlsanity.com/paper/2307.03427/) (2023)

## Built on by (most cited first, 10 of 10)

- [A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction](https://mlsanity.com/paper/2509.00367/) (2025)
- [Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients](https://mlsanity.com/paper/2507.06994/) (2025)
- [RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction](https://mlsanity.com/paper/2505.02529/) (2025)
- [UNICON: UNIfied CONtinual Learning for Medical Foundational Models](https://mlsanity.com/paper/2508.14024/) (2025)
- [MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AI](https://mlsanity.com/paper/2511.11212/) (2025)
- [Discovering Subgroups with Exceptional Survival Characteristics](https://mlsanity.com/paper/2602.22179/) (2026)
- [Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases](https://mlsanity.com/paper/2603.21935/) (2026)
- [CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction](https://mlsanity.com/paper/2605.00350/) (2026)
- [Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis](https://mlsanity.com/paper/2605.04063/) (2026)
- [ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis](https://mlsanity.com/paper/2606.19140/) (2026)

## Abstract (arXiv)

Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage. This knowledge enables the formulation of effective treatment plans that lead to improved patient outcomes. In the past few years, deep learning models have provided a feasible solution for assessing medical images, electronic health records, and genomic data to estimate cancer risk scores. However, these models often fall short of their potential because they struggle to learn regression-aware feature representations. In this study, we propose Survival Rank-N Contrast (SurvRNC) method, which introduces a loss function as a regularizer to obtain an ordered representation based on the survival times. This function can handle censored data and can be incorporated into any survival model to ensure that the learned representation is ordinal. The model was extensively evaluated on a HEad & NeCK TumOR (HECKTOR) segmentation and the outcome-prediction task dataset. We demonstrate that using the SurvRNC method for training can achieve higher performance on different deep survival models. Additionally, it outperforms state-of-the-art methods by 3.6% on the concordance index. The code is publicly available on https://github.com/numanai/SurvRNC