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Contrastive Learnin...
Contrastive Learning of Equivariant Image Representations for Multimodal Deformable Registration
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- Nordling, Love, 1995- (author)
- Uppsala universitet,Avdelningen Vi3,Bildanalys och människa-datorinteraktion,MIDA
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- Öfverstedt, Johan (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
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- Lindblad, Joakim (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
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- Sladoje, Nataša (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- English.
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In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665473583 - 9781665473590
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- We propose a method for multimodal deformable image registration which combines a powerful deep learning approach to generate CoMIRs, dense image-like representations of multimodal image pairs, with INSPIRE, a robust framework for monomodal deformable image registration. We introduce new equivariance constraints to improve the consistency of CoMIRs under deformation. We evaluate the method on three publicly available multimodal datasets, one remote sensing, one histological, and one cytological. The proposed method demonstrates general applicability and consistently outperforms state-of-the-art registration tools \elastixname and VoxelMorph. We share source code of the proposed method and complete experimental setup as open-source at: https://github.com/MIDA-group/CoMIR_INSPIRE.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- image alignment
- correlative imaging
- representation learning
- equivariance
Publication and Content Type
- ref (subject category)
- kon (subject category)
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