Draft:Contrastive Multimodal Image Representation for Registration (CoMIR)

Submission declined on 11 February 2025 by KylieTastic (talk).

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Declined by KylieTastic 2 months ago. Last edited by Citation bot 2 months ago. Reviewer: Inform author.

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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. arXiv:2103.16262. Bibcode:2022PLoSO..1776196L. doi:10.1371/journal.pone.0276196. PMID 36441754.

[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. arXiv:2103.16262. Bibcode:2022PLoSO..1776196L. doi:10.1371/journal.pone.0276196. PMID 36441754.

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