  We present WSSAMNet, a weakly supervised method for medical image
registration. Ours is a two step method, with the first step being the
computation of segmentation masks of the fixed and moving volumes. These masks
are then used to attend to the input volume, which are then provided as inputs
to a registration network in the second step. The registration network computes
the deformation field to perform the alignment between the fixed and the moving
volumes. We study the effectiveness of our technique on the BraTSReg challenge
data against ANTs and VoxelMorph, where we demonstrate that our method performs
competitively.
