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

• URL: http://arxiv.org/abs/2007.06151v1
• Date: Mon, 13 Jul 2020 02:02:00 GMT
• Title: MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation
• Authors: Xingang Yan, Weiwen Jiang, Yiyu Shi, and Cheng Zhuo
• Abstract summary: This paper presents a Multi-Scale NAS framework that is featured with multi-scale search space from network backbone to cell operation. 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.
• Score: 16.206524842952636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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