Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA.
Int J Biostat. 2023 Jun 14;20(1):1-12. doi: 10.1515/ijb-2022-0113. eCollection 2024 May 1.
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.
人们普遍有兴趣使用深度学习来构建医学影像数据的预测模型。这些深度学习方法可以捕捉图像的局部结构，无需手动提取特征。尽管在医学数据分析中对生存建模的重要性，但用于建模成像和事件时间数据关系的深度学习方法的研究仍不够发达。我们提供了一种用于事件时间结果的深度学习方法概述，并通过对神经胶质瘤的组织学数据集进行分析，将几种深度学习方法与基于 Cox 模型的方法进行了比较。
