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

• URL: http://arxiv.org/abs/2308.12440v1
• Date: Wed, 23 Aug 2023 21:47:28 GMT
• Title: HNAS-reg: hierarchical neural architecture search for deformable medical image registration
• Authors: Jiong Wu and Yong Fan
• Abstract summary: This paper presents a hierarchical NAS framework (HNAS-Reg) to identify the optimal network architecture for deformable medical image registration. 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.
• Score: 0.8249180979158817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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