HyperMorph：用于图像配准的分摊超参数学习
HyperMorph: Amortized Hyperparameter Learning for Image Registration
摘要：
我们提出了HyperMorph，这是一种基于学习的可变形图像配准策略，无需在训练过程中调整重要的配准超参数。经典的配准方法解决了一个优化问题，以找到两个图像之间的一组空间对应关系，而基于学习的方法利用训练数据集来学习生成这些对应关系的函数。这两种技术的结果质量在很大程度上取决于超参数的选择。不幸的是，超参数调整是耗时的，并且通常涉及使用各种超参数值训练许多单独的模型，可能导致次优结果。为了解决这种低效率，我们引入了摊销超参数学习图像配准，一种新的策略来学习超参数对变形场的影响。所提出的框架学习一个超网络，该超网络接受一个输入超参数并调制一个配准网络，以产生该超参数值的最佳变形场。实际上，这种策略训练了一个单一的、丰富的模型，可以在测试时从连续的时间间隔中快速、细粒度地发现超参数值。我们证明，这种方法可以用来优化多个超参数大大快于现有的搜索策略，从而减少计算和人力负担，以及增加的灵活性。我们还展示了几个重要的好处，包括提高初始化的鲁棒性，以及快速识别特定于配准任务、数据集甚至单个解剖区域的最佳超参数值的能力，所有这些都无需重新训练HyperMorph模型。我们的代码可在http://voxelmorph.mit.edu上公开获取。
英文摘要：
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical registration methods solve an optimization problem to find a set of spatial correspondences between two images, while learning-based methods leverage a training dataset to learn a function that generates these correspondences. The quality of the results for both types of techniques depends greatly on the choice of hyperparameters. Unfortunately, hyperparameter tuning is time-consuming and typically involves training many separate models with various hyperparameter values, potentially leading to suboptimal results. To address this inefficiency, we introduce amortized hyperparameter learning for image registration, a novel strategy to learn the effects of hyperparameters on deformation fields. The proposed framework learns a hypernetwork that takes in an input hyperparameter and modulates a registration network to produce the optimal deformation field for that hyperparameter value. In effect, this strategy trains a single, rich model that enables rapid, fine-grained discovery of hyperparameter values from a continuous interval at test-time. We demonstrate that this approach can be used to optimize multiple hyperparameters considerably faster than existing search strategies, leading to a reduced computational and human burden as well as increased flexibility. We also show several important benefits, including increased robustness to initialization and the ability to rapidly identify optimal hyperparameter values specific to a registration task, dataset, or even a single anatomical region, all without retraining the HyperMorph model. Our code is publicly available at http://voxelmorph.mit.edu.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
