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NAS-Unet: Neural Architecture Search for Medical Image Segmentation
Open accessAbstract
Neural architecture search (NAS) has significant progress in improving the accuracy of image classification. Recently, some works attempt to extend NAS to image segmentation which shows preliminary feasibility. However, all of them focus on searching architecture for semantic segmentation in natural scenes. In this paper, we design three types of primitive operation set on search space to automatically find two cell architecture DownSC and UpSC for semantic image segmentation especially medical image segmentation. Inspired by the U-net architecture and its variants successfully applied to various medical image segmentation, we propose NAS-Unet which is stacked by the same number of DownSC and UpSC on a U-like backbone network. The architectures of DownSC and UpSC updated simultaneously by a differential architecture strategy during the search stage. We demonstrate the good segmentation results of the proposed method on Promise12, Chaos, and ultrasound nerve datasets, which collected by magnetic resonance imaging, computed tomography, and ultrasound, respectively. Without any pretraining, our architecture searched on PASCAL VOC2012, attains better performances and much fewer parameters (about 0.8M) than U-net and one of its variants when evaluated on the above three types of medical image datasets.
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“NAS-Unet, proposed in 2019, explored differentiable NAS strategies (e.g., DARTS/ProxylessNAS-style methods) on a U-shaped backbone for medical image segmentation [10].”
From Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search (2026) · cites this paper as [10]
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Cites
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InfluentialGoing deeper with convolutions
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BackgroundMethodInfluentialSqueeze-and-Excitation Networks
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BackgroundThe Pascal Visual Object Classes Challenge: A Retrospective
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BackgroundNeural Architecture Search with Reinforcement LearningPreprint
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MnasNet: Platform-Aware Neural Architecture Search for Mobile
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BackgroundEvolving Neural Networks through Augmenting Topologies
Stanley, K. O., & Miikkulainen, R. P. (2002) · Evolutionary Computation · doi:10.1162/106365602320169811 · Cited by 3,269 · cited 1×
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MethodDARTS: Differentiable architecture search
Liu, H., Simonyan, K., & Yang, Y. (2018) · arXiv · doi:10.48550/arxiv.1806.09055 · Cited by 3,248 · cited 16×
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BackgroundMethodInfluentialProgressive Neural Architecture Search
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BackgroundProxylessNAS: Direct Neural Architecture Search on Target Task and HardwarePreprint
Cai, H., Zhu, L., & Han, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00332 · Cited by 1,821 · cited 6×
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BackgroundMethodInfluentialNeural Architecture Search: A Survey
Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv · doi:10.48550/arxiv.1808.05377 · Cited by 1,763
Large-Scale Evolution of Image ClassifiersPreprint
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Background(2012) · neural information processing systems · Cited by 1,291
Brain intelligence: go beyond artificial intelligence
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BackgroundSelf-citationThe Importance of Skip Connections in Biomedical Image Segmentation
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BackgroundAuto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Liu, C., Chen, L.-C., Schroff, F., Adam, H., Hua, W., Yuille, A., & Fei-Fei, L. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00017 · Cited by 1,102 · cited 3×
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MethodHealth-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data
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MethodA Hypercube-Based Encoding for Evolving Large-Scale Neural Networks
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MethodEvaluation of prostate segmentation algorithms for mri: The promise12 challenge
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MethodEfficient Architecture Search by Network Transformation
Cai, H., Chen, T., Zhang, W., Yu, Y. H., & Wang, Y.-X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11709 · Cited by 616 · cited 1×
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MethodPractical Block-Wise Neural Network Architecture Generation
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BackgroundUnderstanding and Simplifying One-Shot Architecture Search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., & Le, Q. V. (2018) · International Conference on Machine Learning · Cited by 558
Neural Architecture OptimizationPreprint
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BackgroundMethodMotor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning
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“Many works have designed network structures for specific tasks [15], [16].”
BackgroundSelf-citationDeep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network
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“recognition of images at the pixel level (the object category to each pixel in the image belongs is marked) [4], [5].”
BackgroundUnderwater image dehazing using joint trilateral filter
Serikawa, S., & Lu, H. (2013) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.10.016 · Cited by 423 · cited 1×
“For the sake of fairness, we re-implement them by Pytorch [55] and use the same data augmentation(we also try to improve the quality of some noisy images [56], [57]).”
MethodNeural Architecture Search with Bayesian Optimisation and Optimal TransportPreprint
Kandasamy, K., Neiswanger, W., Schneider, J., Póczos, B., & Xing, E. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.07191 · Cited by 347 · cited 1×
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MethodSearching for Efficient Multi-Scale Architectures for Dense Image Prediction
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BackgroundResultAutomated model-based vertebra detection, identification, and segmentation in CT images
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MethodSelf-citationSimple And Efficient Architecture Search for Convolutional Neural NetworksPreprint
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MethodCONet: A Cognitive Ocean Network
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Efficient Neural Architecture Search with Network MorphismPreprint
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MethodGradient-based Optimization of Neural Network Architecture.
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Metadata
- Authors
- Weng, Y., Zhou, T., Li, Y., & Qiu, X.
- Affiliations
- Minzu University of China (CN)
- Yangzhou University (CN)
- Shandong University of Traditional Chinese Medicine (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 44247-44257
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2927980542
- Semantic Scholar
- 90d06dca4796e3b0bd75161beb1a1d9264170769
- Cited by
- 487
- References
- 54
- Citations by year
- 2026: 21 · 2025: 54 · 2024: 89 · 2023: 97 · 2022: 72 · 2021: 87 · 2020: 57 · 2019: 10
- FWCI
- 25.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61772575)
- National Key R&D Program of China (2017YFB1402101)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- neural architecture search, medical image segmentation, semantic segmentation, U-Net, magnetic resonance imaging, CT
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