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

arXiv:2403.10603v1 Announce Type: cross
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 …

arxiv contrast cs.ai cs.cv cs.lg prediction survival type