Difference between revisions of "CALAMITI"
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*{{pub| author = L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince| title = Information-based Disentangled Representation Learning for Unsupervised MR Harmonization| conf = ipmi2021}} | *{{pub| author = L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince| title = Information-based Disentangled Representation Learning for Unsupervised MR Harmonization| conf = ipmi2021}} | ||
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Revision as of 16:46, 19 March 2021
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Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI)
Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration (CALAMITI) is our current MR harmonization method. It was designed to achieve unsupervised multi-site MR harmonization. The associated publication is:
- L. Zuo, B.E. Dewey, A. Carass, Y. Liu, Y. He, P.A. Calabresi, and J.L. Prince, "Information-based Disentangled Representation Learning for Unsupervised MR Harmonization", 27th Conference on Information Processing in Medical Imaging (IPMI 2021), Virtually in Bornholm, Denmark, June 28 - July 2, 2021.
Software
- Coming soon.
Instructions
- Coming soon.
