论文标题
部分可观测时空混沌系统的无模型预测
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions
论文作者
论文摘要
Deformable image registration plays a critical role in various tasks of medical image analysis.从常规能源优化或深层网络中得出的成功的注册算法需要从计算机专家那里进行巨大的努力来井设计注册能源，或者仔细调整特定类型的医疗数据类型的网络架构。为了解决上述问题，本文提出了一种自动学习登记算法（Autoreg），该算法（Autoreg）合作优化了体系结构及其相应的培训目标，从而使非计算机专家，例如医学/临床用户，以方便地查找现有风景的现有注册算法的各种风景。具体而言，我们建立了三级框架，以自动搜索机制和合作优化来推导注册网络体系结构和目标。 We conduct image registration experiments on multi-site volume datasets and various registration tasks.广泛的结果表明，与主流UNET体系结构相比，我们的自动化可以自动学习以给定体积的最佳深度注册网络并实现最先进的性能，也可以显着提高计算效率（从0.558到0.558至0.270秒，对于同一配置的3D图像对）。
Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures for the specific type of medical data. To tackle the aforementioned problems, this paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts, e.g., medical/clinical users, to conveniently find off-the-shelf registration algorithms for diverse scenarios. Specifically, we establish a triple-level framework to deduce registration network architectures and objectives with an auto-searching mechanism and cooperating optimization. We conduct image registration experiments on multi-site volume datasets and various registration tasks. Extensive results demonstrate that our AutoReg may automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance, also significantly improving computation efficiency than the mainstream UNet architectures (from 0.558 to 0.270 seconds for a 3D image pair on the same configuration).
