Rotation-equivariant convolutions in deformable brain MRI registration networks deliver higher accuracy with fewer parameters, greater robustness to rotations, and better performance on limited training data.
We evaluated this approach by replacing standard encoders in three baseline architectures (V oxelMorph, Dual- PRNet++, and RDP Net) across multiple brain MRI datasets
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Rotation Equivariant Convolutions in Deformable Registration of Brain MRI
Rotation-equivariant convolutions in deformable brain MRI registration networks deliver higher accuracy with fewer parameters, greater robustness to rotations, and better performance on limited training data.