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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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Cited by
Sorted by citations
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“…that address more complex tasks with image data such as object detection and segmentation (Zoph et al., 2018; Tan et al., 2018; Liu et al., 2019a; Weng et al., 2019), as well as works that address safety-critical issues such as the discovery of architectures that are robust against adversarial…”
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A survey on deep learning for skin lesion segmentation
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“Among the existing research works in this area, NAS-UNet [111] is based on the design of three types of primitive operation set on search space to automatically find two cell architectures (DownSC and UpSC) for semantic segmen-”
MethodC2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
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BackgroundAdaEn-Net: An ensemble of adaptive 2D–3D Fully Convolutional Networks for medical image segmentation
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MethodInfluentialAutomated Machine Learning for Healthcare and Clinical Notes Analysis
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MFSNet: A multi focus segmentation network for skin lesion segmentation
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MethodResUNet+: A New Convolutional and Attention Block-Based Approach for Brain Tumor Segmentation
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Semantic Segmentation on Panoramic Dental X-Ray Images Using U-Net Architectures
Zannah, R., Bashar, M., Mushfiq, R. B., Chakrabarty, A., Hossain, S., & Jung, Y. J. (2024) · IEEE Access · doi:10.1109/access.2024.3380027 · Cited by 47
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BackgroundAutomatic void content assessment of composite laminates using a machine-learning approach
Machado, J., Tavares, J., Camanho, P., & Correia, N. (2022) · Composite structures · doi:10.1016/j.compstruct.2022.115383 · Cited by 39
Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging
Xiao, A., Shen, B., Shi, X., Zhang, Z., Zhang, Z., Tian, J., Ji, N., & Hu, Z. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3166129 · Cited by 41
“Some recent studies in the medical field have also used NAS and achieved remarkable results in the segmentation of MRI and CT images [40]–[42].”
Yu, C., Wang, Y., Tang, C., Feng, W., & Lv, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107579 · Cited by 46
Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies
Kendrick, J., Francis, R. J., Hassan, G. M., Rowshanfarzad, P., Jeraj, R., Kasisi, C., Rusanov, B., & Ebert, M. A. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.771787 · Cited by 41
Deep learning for gastroscopic images: computer-aided techniques for clinicians
Jin, Z., Gan, T., Wang, P., Fu, Z., Zhang, C., Yan, Q., Zheng, X., Liang, X., & Ye, X. (2022) · BioMedical Engineering OnLine · doi:10.1186/s12938-022-00979-8 · Cited by 41
“For example, NASUNet [125], AutoDeepLab [126], MS-NAS [127], and BiX-NAS [128] have achieved SOTA performance on medical image segmentation.”
BackgroundPocketNet: A Smaller Neural Network for Medical Image Analysis
Celaya, A., Actor, J., Rajarajeswari, M., Gates, E. D. H., Chung, C. D., Schellingerhout, D., Rivière, B. M., & Fuentes, D. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3224873 · Cited by 47
Visual geometry Group-UNet: Deep learning ultrasonic image reconstruction for curved parts
Mei, Y., Jin, H., Yu, B., et al. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0004827 · Cited by 39
Tutorial on the Use of Deep Learning in Diffuse Optical Tomography
Balasubramaniam, G. M., Wiesel, B., Biton, N., Kumar, R., Kupferman, J., & Arnon, S. (2022) · Electronics · doi:10.3390/electronics11030305 · Cited by 41
Ruan, D., Han, J., Yan, J., & Gühmann, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-31532-9 · Cited by 40
Liu, X., Liu, X., Yao, C., et al. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102307 · Cited by 38
Multi-Scale Network for Thoracic Organs Segmentation
Khalil, M. I., Tehsin, S., Humayun, M., Jhanjhi, N. Z., & AlZain, M. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.020561 · Cited by 38
“Segmentation segments of the image into cohesive regions, clustering techniques may be extended to subdivision by removing the overall features of the object to efficiently distinguish ROI from the object context [5].”
Yu, Z., Lee, F., & Chen, Q. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04570-z · Cited by 38
Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks
Varshney, D., Rahnemoonfar, M., Yari, M., et al. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378070 · Cited by 37
“strategy, combined with data augmentation, turned out to be useful in biomedical image segmentation, especially for detecting tissue deformations. The success of UNet in biomedical images [31], [42], [43] led to its application and improvement for other domains such as remote sensing [44], [45] and autonomous driving…”
MethodLu, Z., Xia, W., Huang, Y., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3219286 · Cited by 37
“In the field of medical imaging, most works were proposed for image segmentation and classification [13, 17, 35, 55, 59, 61].”
BackgroundMethodInfluentialYOLO‐UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image
Iriawan, N., Pravitasari, A. A., Nuraini, U. S., Nirmalasari, N. I., Azmi, T., Nasrudin, M., Fandisyah, A. F., Fithriasari, K., Purnami, S. W., Irhamah, I., et al. (2024) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2024/3819801 · Cited by 44
Learning deep morphological networks with neural architecture search
Hu, Y., Belkhir, N., Angulo, J., et al. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108893 · Cited by 36
“An alternative approach focuses on automatically finding the network architecture design using Neural Architecture Search (NAS) methods Liu et al. (2019a); Luo et al. (2019); Liu et al. (2018); Pham et al. (2018); Weng et al. (2019) in lieu of manual design.”
MethodMukasa, P., Wakholi, C., Faqeerzada, M. A., Amanah, H. Z., Kim, H., Joshi, R., Suh, H. K., Kim, G., Lee, H., Kim, M. S., et al. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106799 · Cited by 36
Multi-Scale Fusion U-Net for the Segmentation of Breast Lesions
Li, J., Cheng, L., Xia, T., Ni, H., & Li, J. (2021) · IEEE Access · doi:10.1109/access.2021.3117578 · Cited by 36
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search
Heuillet, A., Nasser, A., Arioui, H., & Tabia, H. (2024) · ACM Computing Surveys · doi:10.1145/3665138 · Cited by 46
Discretization-aware architecture search
Tian, Y., Liu, C., Xie, L., Jiao, J., & Ye, Q. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108186 · Cited by 34
Cheng, P., Myronenko, A., Hatamizadeh, A., Nath, V., Siddiquee, M. M. R., He, Y., Xu, D., Chellappa, R., & Yang, D. (2022) · 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/cvpr52688.2022.02008 · Cited by 35
“Early attempts [21, 37, 44, 47] build on the prior architecture of U-Net, and modify edge operations and connections while preserving the overall U-shape structure.”
BackgroundWhen Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design
Zhang, X., Jiang, W., Shi, Y., & Hu, J. (2019) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2019.00014 · Cited by 37
“Most recently, neural architecture search techniques are extended for more complicated tasks, such as Image Segmentation [14, 43].”
BackgroundZhang, B., Rahmatullah, B., Wang, S. L., Zhang, G., Wang, H., & Ebrahim, N. A. (2021) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13394 · Cited by 35
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
Wang, X., Xiang, T., Zhang, C., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_22 · Cited by 34
“Similar to the nuclei segmentation, Table 2 indicates that BiX-Net achieves the best performance of all classes among state-of-the-art NAS searched networks [16,9,18] with much lower computational complexity.”
MethodCombining contrastive learning and shape awareness for semi-supervised medical image segmentation
Chen, Y., Chen, F., & Huang, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122567 · Cited by 34
Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment
Vaze, S., Xie, W., & Namburete, A. I. L. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2961264 · Cited by 33
“and familiar taskwhich has beenwidely studied in the ultrasound analysis literature since its publication [13], [14].”
BackgroundYing, W., Zheng, Q., Wu, Y., Yang, K., Zhou, Z., Chen, J., & Ye, Z. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110869 · Cited by 32
DEGAS: differentiable efficient generator search
Doveh, S., & Giryes, R. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06309-8 · Cited by 33
“These include semantic image segmentation [28], medical image segmentation [48], volumetric medical image segmentation [52], object detection [34], and active learning [14].”
BackgroundMulti-scale conv-attention U-Net for medical image segmentation
Pan, P., Zhang, C., Sun, J.-B., & Guo, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-96101-8 · Cited by 48
“Due to its powerful segmentation performance, U-net has been widely used as an indispensable tool in the field of medical image processing and has achieved remarkable success in numerous 2D semantic segmentation tasks 12–14 .”
SliceMamba With Neural Architecture Search for Medical Image Segmentation
Fan, C., Yu, H., Huang, Y., Wang, L., Yang, Z., & Jia, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3564381 · Cited by 42
“Additionally, Weng et al. [49] introduced NAS-UNet, which utilizes neural architecture search (NAS) to discover optimal model architectures tailored to different datasets automatically.”
AutoSegNet: An Automated Neural Network for Image Segmentation
Xu, Z., Zuo, S., Lam, E. Y. M., Lee, B., & Chen, N. (2020) · IEEE Access · doi:10.1109/access.2020.2995367 · Cited by 29
“NAS-Unet [24] searches a U-like backbone network for medical image segmentation, and V-NAS [25] formulates the structure learning as differentiable neural architecture search, allowing the network to choose among 2D, 3D or Pseudo-3D (P3D) convolutions at each layer.”
MethodWu, J., Liu, W., Li, C., et al. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09284-4 · Cited by 30
Varshney, D., Rahnemoonfar, M., Yari, M., Paden, J., Ibikunle, O., & Li, J. (2021) · Remote Sensing · doi:10.3390/rs13142707 · Cited by 36
“UNet’s success in processing biomedical images [32,33] inspired its application in remote sensing [34,35] and autonomous driving [36], among other domains.”
BackgroundYu, Y., Chen, S., & Wei, H. (2022) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2022.102300 · Cited by 28
Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
Hadinata, P. N., Simanta, D., Eddy, L., & Nagai, K. (2023) · Applied Sciences · doi:10.3390/app13042398 · Cited by 29
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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