SOTAVerified

Pretraining Deformable Image Registration Networks with Random Images

2025-05-30Code Available0· sign in to hype

Junyu Chen, Shuwen Wei, Yihao Liu, Aaron Carass, Yong Du

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Recent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images. Previous work showed that DNNs trained on randomly generated images with carefully designed noise and contrast properties can still generalize well to unseen medical data. Building on this insight, we propose using registration between random images as a proxy task for pretraining a foundation model for image registration. Empirical results show that our pretraining strategy improves registration accuracy, reduces the amount of domain-specific data needed to achieve competitive performance, and accelerates convergence during downstream training, thereby enhancing computational efficiency.

Tasks

Reproductions