There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed. We provide an overview of deep learning methods for time-to-event outcomes and compare several deep learning methods to Cox model based methods through the analysis of a histology dataset of gliomas.

中文翻译：

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使用深度学习和医学成像进行生存分析

人们普遍对使用深度学习为医学影像数据建立预测模型感兴趣。这些深度学习方法捕获图像的局部结构，不需要手动提取特征。尽管在医学数据分析的背景下对生存建模很重要，但对成像和事件发生时间数据关系建模的深度学习方法的研究仍不发达。我们概述了用于事件发生时间结果的深度学习方法，并通过对神经胶质瘤组织学数据集的分析将几种深度学习方法与基于 Cox 模型的方法进行了比较。