WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 3 of its 12 references that are in the map. First posted on arXiv 5 Mar 2022.
Builds on
3 of 12 references.
References · 3
- 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
- 2018Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registrationcited by 90
- 2021The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patientscited by 32
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Citing papers per year: 2026: 2.
2 papers.