Though we both used the deepsurv network to predict time-to-event endpoints, VAE was used for dimension reduction for the clinical and radiomics input in our case, whereas the genomic data was directly fed to the fully connected layers in their model. For the visualization of the patterns in the images, integrated gradient method was used in our study, which surpasses the original heatmap method since the direction provided by the integrated gradients lead better towards the global optimum than the normal gradient which may lead to local optima that is used in basic heat map method. Additionally, the input data (CECT imaging and clinical data vs. histology imaging and genomics data) is different as well.

尽管我们都使用了deepsurv网络来预测时间事件终点，但我们在临床和影像组学输入方面使用了VAE进行维度缩减，而他们的模型直接将基因组数据输入到全连接层中。对于图像中的模式可视化，我们在研究中使用了综合梯度方法，它优于原始的热图方法，因为综合梯度所提供的方向更好地指向全局最优解，而普通梯度可能导致局部最优解，这在基本热图方法中使用。此外，输入数据（CECT成像和临床数据与组织学成像和基因组数据）也是不同的。