HNAS-reg: hierarchical neural architecture search for deformable medical
  image registration
Jiong Wu Yong Fan
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.