# MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2007.06151 · HTML: https://mlsanity.com/paper/2007.06151/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices](https://mlsanity.com/paper/2202.11317/) (2022)
- [Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search](https://mlsanity.com/paper/2107.06871/) (2021)
- [HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet](https://mlsanity.com/paper/2112.10652/) (2021)
- [BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2106.14033/) (2021)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising](https://mlsanity.com/paper/2103.12995/) (2021)
- [Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond](https://mlsanity.com/paper/2203.05709/) (2022)
- [RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2206.04682/) (2022)
- [MRF-UNets: Searching UNet with Markov Random Fields](https://mlsanity.com/paper/2207.06168/) (2022)
- [Mixed-Block Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2202.11401/) (2022)
- [Meta-Learning of NAS for Few-shot Learning in Medical Image Applications](https://mlsanity.com/paper/2203.08951/) (2022)
- [HNAS-reg: hierarchical neural architecture search for deformable medical image registration](https://mlsanity.com/paper/2308.12440/) (2023)
- [One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification](https://mlsanity.com/paper/2506.14176/) (2025)
- [Heed the Noise in Performance Evaluations in Neural Architecture Search](https://mlsanity.com/paper/2202.02078/) (2022)
- [SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation](https://mlsanity.com/paper/2309.04672/) (2023)

## Abstract (arXiv)

The recent breakthroughs of Neural Architecture Search (NAS) have motivated various applications in medical image segmentation. However, most existing work either simply rely on hyper-parameter tuning or stick to a fixed network backbone, thereby limiting the underlying search space to identify more efficient architecture. This paper presents a Multi-Scale NAS (MS-NAS) framework that is featured with multi-scale search space from network backbone to cell operation, and multi-scale fusion capability to fuse features with different sizes. To mitigate the computational overhead due to the larger search space, a partial channel connection scheme and a two-step decoding method are utilized to reduce computational overhead while maintaining optimization quality. Experimental results show that on various datasets for segmentation, MS-NAS outperforms the state-of-the-art methods and achieves 0.6-5.4% mIOU and 0.4-3.5% DSC improvements, while the computational resource consumption is reduced by 18.0-24.9%.