Figure 5.

Three methods to overcome the complications of limited data sets. Transfer learning takes a model trained on a large data set and repurposes it for a new task, replacing only the final layer. Self‐supervised learning is a type of transfer learning; however, the data set used in pre‐training does not need to have labels—here the task is simply to recognize that 2 versions of the same image are indeed the same image, and in doing so the model learns to recognize invariant features. Increasing data set size can be done in a number of ways; however, pooling data across institutions has technical, logistical, and privacy issues that must be overcome. Circles represent individual nodes. Color figure can be viewed in the online issue, which is available at http://onlinelibrary.wiley.com/doi/10.1002/art.42296/abstract.