Figure 1:

Schematic diagram of the proposed method. We represent every image as a graph of patches. The context is imposed by anatomical correspondences among patients via registration and graph-based hierarchical model used to incorporate the relationship between different anatomical regions. We use a conditional encoder E(·,·) to learn patch-level textural features and use graph convolutional network G(·,·) to learn graph-level representation through contrastive learning objectives. The detailed architecture of the networks are presented in Supplementary Material.