General Vision Encoder Features as Guidance in Medical Image Registration

General vision encoders like DINOv2 and SAM have recently transformed computer vision. Even though they are trained on natural images, encoder models have excelled in medical imaging, e.g., in classification, segmentation, and registration.

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Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A. Schnabel, Veronika A. Zimmer (2024). Dataset: General Vision Encoder Features as Guidance in Medical Image Registration. https://doi.org/10.57702/g5wv2w7q

DOI retrieved: December 2, 2024

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Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2407.13311
Author Fryderyk Kögl
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Anna Reithmeir
Vasiliki Sideri-Lampretsa
Ines Machado
Rickmer Braren
Daniel Rückert
Julia A. Schnabel
Veronika A. Zimmer
Homepage https://github.com/compai-lab/2024-miccai-koegl