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.
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis,
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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.