bibtext: @InProceedings{ Sae_SurvRNC_MICCAI2024,
                   author = { Saeed, Numan and Ridzuan, Muhammad and Maani, Fadillah Adamsyah and Alasmawi, Hussain and Nandakumar, Karthik and Yaqub, Mohammad },
                   title = { { SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 at https://github.com/numanai/SurvRNC.
title: SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
authors: Saeed, Numan Ridzuan, Muhammad Maani, Fadillah Adamsyah Alasmawi, Hussain Nandakumar, Karthik Yaqub, Mohammad