Architectural and Regularization Components in Deep Learning Medical Image Registration: Systematic Ablation Study
Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stages, and training-objective additions such as regularization losses. Papers tend to adopt both at once, so it is unclear which is doing the work. I ran a controlled ablation to separate them. Using the OASIS brain MRI dataset (394 training subjects, 20 test subjects), I trained four variants of the same registration pipeline: a baseline 3D U-Net with basic similarity losses, the same U-Net with a full regularization suite, an affine-plus-deformable architecture with basic losses, and the affine architecture with the full suite. I evaluated ...
| Authors | Nabira Rashid |
|---|---|
| Primary category | cs.CV |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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