Most machine learning algorithms have been developed to perform classification or regression. However, in clinical research we often want to estimate the time to and event, such as death or recurrence of cancer, which leads to a special type of learning task that is distinct from classification and regression. This task is termed survival analysis, but is also referred to as time-to-event analysis or reliability analysis. Many machine learning algorithms have been adopted to perform survival analysis: Support Vector Machines, Random Forest, or Boosting. It has only been recently that survival analysis entered the era of deep learning, which is the focus of this post.
You will learn how to train a convolutional neural network to predict time to a (generated) event from MNIST images, using a loss function specific to survival analysis. The first part, will cover some basic terms and quantities used in survival analysis (feel free to skip this part if you are already familiar). In the second part, we will generate synthetic survival data from MNIST images and visualize it. In the third part, we will briefly revisit the most popular survival model of them all and learn how it can be used as a loss function for training a neural network. Finally, we put all the pieces together and train a convolutional neural network on MNIST and predict survival functions on the test data.