Draft:Contrastive Multimodal Image Representation for Registration (CoMIR)
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Last edited by Love Nordling UU (talk | contribs) 3 months ago. (Update) |
Contrastive Multimodal Image Representation for Registration (CoMIR) is a deep learning framework designed to align images from different modalities by transforming them into a shared representational space. The framework archives this using contrastive learning that maximizes the similarity between image representations of aligned multimodal image pairs.[1] These image representations can be used with any typical intensity-based image registration method.
References
- ^ Lu, Jiahao; Pielawski, Nicki; Wetzer, Emil; Öfverstedt, Johan; Wählby, Carolina; Lindblad, Joakim; Sladoje, Natasa (2022). "Is image-to-image translation the panacea for multimodal image registration? A comparative study". PLOS ONE. 17 (11): e0276196. doi:10.1371/journal.pone.0276196. Retrieved 11 February 2025.
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