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Found 4 papers, 4 papers with code
Controllable cardiac synthesis via disentangled anatomy arithmetic
Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e. g. MRI), plausible new cardiac images are created with the target characteristics.
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
We explicitly model the representations related to domain shifts.
Measuring the Biases and Effectiveness of Content-Style Disentanglement
In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.
Disentangled Representations for Domain-generalized Cardiac Segmentation
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains.
