A latent graph representation of chest X-rays, with a learned topology, is used to generate structure-preserving synthetic images that improve data augmentation for classification and segmentation.
Towards graph pooling by edge contraction,
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Structure-Preserving Medical Image Generation from a Latent Graph Representation
A latent graph representation of chest X-rays, with a learned topology, is used to generate structure-preserving synthetic images that improve data augmentation for classification and segmentation.