Draft:Contrastive Multimodal Image Representation for Registration (CoMIR)

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Instructions · What links here · Contrastive Multimodal Image Representation for Registration (CoMIR) (talk: + · bio) · (log) · Copyvios report · reFill · Citation Bot · (Search: Google, Wikipedia) · Submitted 3 months ago by Love Nordling UU (talk: D · +) · Last edited 3 months ago by Love Nordling UU

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.{{cite journal}}: CS1 maint: unflagged free DOI (link)

[Lu2022-1] 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)

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