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

arXiv: https://arxiv.org/abs/2308.12440 · HTML: https://mlsanity.com/paper/2308.12440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration](https://mlsanity.com/paper/1805.04605/) (2018)
- [Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks](https://mlsanity.com/paper/2003.09514/) (2020)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2007.06151/) (2020)
- [ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation](https://mlsanity.com/paper/2002.05773/) (2020)
- [Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions](https://mlsanity.com/paper/2203.06810/) (2022)
- [MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision](https://mlsanity.com/paper/2010.01465/) (2020)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks](https://mlsanity.com/paper/2404.04244/) (2024)

## Abstract (arXiv)

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.