Convolutional neural networks (CNNs) have been widely used to build deep
learning models for medical image registration, but manually designed network
architectures are not necessarily optimal. This paper presents a hierarchical
NAS framework (HNAS-Reg), consisting of both convolutional operation search and
network topology search, to identify the optimal network architecture for
deformable medical image registration. To mitigate the computational overhead
and memory constraints, a partial channel strategy is utilized without losing
optimization quality. Experiments on three datasets, consisting of 636
T1-weighted magnetic resonance images (MRIs), have demonstrated that the
proposal method can build a deep learning model with improved image
registration accuracy and reduced model size, compared with state-of-the-art
image registration approaches, including one representative traditional
approach and two unsupervised learning-based approaches.

这篇论文提出了一个层次化的 NAS 框架（HNAS-Reg），通过卷积操作搜索和网络拓扑搜索，寻找最优网络结构用于可变形医学图像配准，实验证实该方法相比传统方法和两种无监督学习方法，在配准准确性和模型大小方面有明显的改进。

HNAS-reg：用于可变形医学图像配准的分层神经架构搜索

HNAS-reg: hierarchical neural architecture search for deformable medical  image registration

To reduce the human efforts in neural network design, Neural Architecture
Search (NAS) has been applied with remarkable success to various high-level
vision tasks such as classification and semantic segmentation. The underlying
idea for the NAS algorithm is straightforward, namely, to enable the network
the ability to choose among a set of operations (e.g., convolution with
different filter sizes), one is able to find an optimal architecture that is
better adapted to the problem at hand. However, so far the success of NAS has
not been enjoyed by low-level geometric vision tasks such as stereo matching.
This is partly due to the fact that state-of-the-art deep stereo matching
networks, designed by humans, are already sheer in size. Directly applying the
NAS to such massive structures is computationally prohibitive based on the
currently available mainstream computing resources. In this paper, we propose
the first end-to-end hierarchical NAS framework for deep stereo matching by
incorporating task-specific human knowledge into the neural architecture search
framework. Specifically, following the gold standard pipeline for deep stereo
matching (i.e., feature extraction -- feature volume construction and dense
matching), we optimize the architectures of the entire pipeline jointly.
Extensive experiments show that our searched network outperforms all
state-of-the-art deep stereo matching architectures and is ranked at the top 1
accuracy on KITTI stereo 2012, 2015 and Middlebury benchmarks, as well as the
top 1 on SceneFlow dataset with a substantial improvement on the size of the
network and the speed of inference. The code is available at
this https URL

本文提出了一个双目立体匹配中第一个基于任务特定的人类知识的端对端分层神经架构搜索框架，通过优化整个管道的架构，我们的搜索网络优于所有现有的深度立体匹配结构，在多个基准测试中排名前 1，并在网络规模和推理速度上作出了实质性的改进。