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.

我们提出了 IMLSP，一个可解释的多标签多模态深度存活预测框架，用于同时预测多个头颈癌存活结果，并提供深度预测过程的时间事件特定的可视化解释。我们采用多任务逻辑回归层将存活预测从回归问题转化为多时间点分类任务，并能够同时预测多个相关存活结果。通过公开可用的 RADCURE HNC 数据集的评估，我们的方法在所有存活结果上优于相应的单模态模型和单标签模型。生成的激活图显示，该模型在做出决策时主要关注肿瘤和淋巴结体积，并且对于高风险患者和低风险患者的感兴趣体积有所不同。我们证明多标签学习策略可以提高学习效率和预后性能，而可解释的存活预测模型有助于理解人工智能的决策过程，并促进个性化治疗。