# Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation

arXiv: https://arxiv.org/abs/2405.05488 · HTML: https://mlsanity.com/paper/2405.05488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 9 May 2024.

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

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework](https://mlsanity.com/paper/1801.05512/) (2018)
- [M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning](https://mlsanity.com/paper/2007.00453/) (2020)
- [Multi-Label Learning from Medical Plain Text with Convolutional Residual Models](https://mlsanity.com/paper/1801.05062/) (2018)
- [Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks](https://mlsanity.com/paper/2012.05765/) (2020)

## Built on by (most cited first, 3 of 3)

- [AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer](https://mlsanity.com/paper/2604.09280/) (2026)
- [CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction](https://mlsanity.com/paper/2605.00350/) (2026)
- [Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer](https://mlsanity.com/paper/2607.26276/) (2026)

## Abstract (arXiv)

A comprehensive and reliable survival prediction model is of great importance to assist in the personalized management of Head and Neck Cancer (HNC) patients treated with curative Radiation Therapy (RT). In this work, we propose IMLSP, an Interpretable Multi-Label multi-modal deep Survival Prediction framework for predicting multiple HNC survival outcomes simultaneously and provide time-event specific visual explanation of the deep prediction process. We adopt Multi-Task Logistic Regression (MTLR) layers to convert survival prediction from a regression problem to a multi-time point classification task, and to enable predicting of multiple relevant survival outcomes at the same time. We also present Grad-TEAM, a Gradient-weighted Time-Event Activation Mapping approach specifically developed for deep survival model visual explanation, to generate patient-specific time-to-event activation maps. We evaluate our method with the publicly available RADCURE HNC dataset, where it outperforms the corresponding single-modal models and single-label models on all survival outcomes. The generated activation maps show that the model focuses primarily on the tumor and nodal volumes when making the decision and the volume of interest varies for high- and low-risk patients. We demonstrate that the multi-label learning strategy can improve the learning efficiency and prognostic performance, while the interpretable survival prediction model is promising to help understand the decision-making process of AI and facilitate personalized treatment.