HNAS-REG: Hierarchical Neural Architecture Search for Deformable Medical Image Registration
Yong Fan
-
SPS
IEEE Members: $11.00
Non-members: $15.00Length: 00:02:08
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 operations search and network topologies 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.