Article: HNAS-Reg: Hierarchical Neural Architecture Search for Deformable Medical Image Registration.
Proceedings. IEEE International Symposium on Biomedical Imaging
2023 Volume 2023
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), ...
| 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. |
|---|---|
| Language | English |
| Publishing date | 2023-09-01 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 1945-7928 |
| ISSN | 1945-7928 |
| DOI | 10.1109/isbi53787.2023.10230534 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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