用随机图像预训练注册模型，提升医学图像配准效率
Pretraining Deformable Image Registration Networks with Random Images
- 以随机图像间的配准作为预训练任务
- 显著提高下游任务精度并减少所需医学数据量
- 适合追求高效训练的医学影像研究者
基于深度学习的医学图像配准近年来表明，训练深度神经网络（DNN）并不一定需要医学图像。先前研究显示，使用具有精心设计噪声和对比度特性的随机生成图像训练的DNN，仍能在未见医学数据上良好泛化。基于此洞察，我们提出将随机图像间的配准作为代理任务，用于预训练图像配准的基础模型。实验结果表明，该预训练策略可提升配准精度，减少达到竞争性性能所需的领域特定数据量，并加速下游训练收敛，从而增强计算效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。