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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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Cite this as

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

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Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Defined In	https://doi.org/10.48550/arXiv.2407.13311
Author	Fryderyk Kögl
More Authors	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