Figure 6.

The prediction network (4Dsurvival) is a denoising autoencoder that takes time-resolved cardiac motion meshes as its input (right ventricle shown in solid white, left ventricle in red). For the sake of simplicity two hidden layers, one immediately preceding and the other immediately following the central layer (latent code layer), have been excluded from the diagram. The autoencoder learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction that is robust to noise. The actual number of latent factors is treated as an optimisable parameter.