Difference between revisions of "CALAMITI"

Revision as of 00:51, 25 March 2021

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", 27^th Conference on Information Processing in Medical Imaging (IPMI 2021), Virtually in Bornholm, Denmark, June 28 - July 2, 2021.

Coming soon.

Coming soon. If you have questions regarding the method or software, please email Lianrui Zuo at lr_zuo@jhu.edu

Revision as of 16:58, 19 March 2021 (view source) Aaron (talk \| contribs) (WS.) ← Older edit		Revision as of 00:51, 25 March 2021 (view source) Lianrui (talk \| contribs) Newer edit →
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	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:		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:

−	*{{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 = [https://arxiv.org/abs/2103.13283 Information-based Disentangled Representation Learning for Unsupervised MR Harmonization]\| conf = ipmi2021}}