Communication Dans Un Congrès Année : 2024

Analysis of Disentangled Representation Learning for High-Resolution Dynamic MRI Synthesis

Résumé

Unpaired image synthesis is a particularly active area of research, especially in medical imaging, where paired datasets are rare. Disentangled representations are an important part of the techniques used, following those based on GAN. However, by relying on the factorization of an image into independent variation latent codes, these methods can offer greater control over the synthesis result than GANs. This work investigates the use of disentangled representation learning for high-resolution dynamic MRI synthesis.

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Dates et versions

hal-04598556 , version 1 (03-06-2024)

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Claire Scavinner-Dorval, Rodolphe Bailly, Bhushan Borotikar, Sylvain Brochard, Douraied Ben Salem, et al.. Analysis of Disentangled Representation Learning for High-Resolution Dynamic MRI Synthesis. SPIE Medical Imaging 2024, Feb 2024, San Deigo, CA,, United States. ⟨10.1117/12.3006829⟩. ⟨hal-04598556⟩
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