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We present WSSAMNet, a weakly supervised method for medical imageregistration. Ours is a two step method, with the first step being thecomputation of segmentation masks of the fixed and moving volumes. These masksare then used to attend to the input volume, which are then provided as inputsto a registration network in the second step. The registration network computesthe deformation field to perform the alignment between the fixed and the movingvolumes. We study the effectiveness of our technique on the BraTSReg challengedata against ANTs and VoxelMorph, where we demonstrate that our method performscompetitively.

WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network