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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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“The baseline methods are U-Net [8], FC-Densenet [27].”
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InfluentialGoing deeper with convolutions
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“From the earliest LeNet [9] to AlexNet [10], VggNet [11], GoogleNet [12], ResNet [13] and the recent DenseNet [14], the performance of CNN models getting stronger and more mature.”
Fully convolutional networks for semantic segmentation
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“Different from FCNs, the number of down-sampling layers and up-sampling layers and convolution layers between them in the U-net is the same.”
BackgroundMethodInfluentialSqueeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · cited 2×
“conv [47] and cweight [48], are prevalent in current NAS methods.”
MethodChen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · cited 1×
“conv [47] and cweight [48], are prevalent in current NAS methods.”
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Khoong, W. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01581 · Cited by 10
“Recent advancements in deep learning approaches for semantic segmentation include the fully convolutional neural network [10] (CNN), U-Net [12] which obtained the highest accuracy in the segmentation of neuronal structures in electron microscopic stacks, V-Net [11] which is a 3D extension of U-Net to predict segmentat…”
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“UNet has been widely used in various tasks including image classification and segmentation [9,10,11,12,13].”
MethodDT-Net: Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation
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Exploring the Intersection Between Neural Architecture Search and Continual Learning
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Small aerial object detection through GAN-integrated feature pyramid networks
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Melanoma lesion localization using UNet and explainable AI
Kibriya, H., Siddiqa, A., & Khan, W. Z. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11080-1 · Cited by 9
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“[16] propose NAS-Unet, which uses two cell architectures, DownSC and UpSC, updated by a differential architecture strategy during the search stage, employed primarily for medical image segmentation.”
MethodHRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation
Lyu, C., Hu, G., & Wang, D. (2020) · IEEE Access · doi:10.1109/access.2020.2975983 · Cited by 9
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“NAS-Unet [13] is one of studies that extends NAS into medical domain.”
BackgroundNeural Network-Based Price Tag Data Analysis
Laptev, P., Litovkin, S., Davydenko, S., et al. (2022) · Future Internet · doi:10.3390/fi14030088 · Cited by 9
Cheng, J., Wang, H., Zhu, Y., Liu, Q., Zhang, Q., Su, T., Chen, J., Ge, Y., Hu, Z., Liu, X., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08143 · Cited by 11
“Several novel NAS methods, such as reinforcement learning and evolutionary computation, have been developed and applied in a variety of deep learning tasks [32-34].”
MethodEnhanced pediatric thyroid ultrasound image segmentation using DC-Contrast U-Net
Peng, B., Wu, L., Zhou, W., Bai, Y., Luo, A., Xie, S., & Yin, L. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01415-0 · Cited by 8
Dharaneswar, S., & Kumar, B. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107045 · Cited by 8
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Adaptive decoder-block selection with filter reweighting for medical image segmentation
Chen, Y., Ma, B., & Xia, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105033 · Cited by 8
HNAS-Reg: Hierarchical Neural Architecture Search for Deformable Medical Image Registration
Wu, J., & Yong Fan, A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230534 · Cited by 8
“To facilitate the cell-level search procedure, we follow the idea of continuous relaxation in NAS-Unet [10] in our method.”
MethodInfluentialPrideDiff: Physics-Regularized Generalized Diffusion Model for CT Reconstruction
Lu, Z., Gao, Q., Wang, T., Yang, Z., Wang, Z., Yu, H. J., Chen, H., Zhou, J., Shan, H., & Zhang, Y. (2024) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2024.3471677 · Cited by 9
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A comparison of manual and automated neural architecture search for white matter tract segmentation
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Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search
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“To overcome this problem, NAS has been studied in various applications, such as object detection [8], semantic segmentation [17], and medical image segmentation [36, 35]; however, there is still a need to utilize NAS techniques in more real-world scenarios.”
MethodAn accurate Cluster chaotic optimization approach for digital medical image segmentation
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Neural architecture search for medicine: A survey
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“Our segmentation model uses a UNet [2, 8, 19, 24] architecture, taking input images from our prototype to generate a segmented mask with white pixels representing skin and black pixels representing the background.”
MVDI25K: A large-scale dataset of microscopic vaginal discharge images
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Neural Network Model for Artifacts Marking in EEG Signals
Komisaruk, O., & Nikulchev, E. V. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0121204 · Cited by 7
“Then the data is converted into a one-dimensional vector using the Flatten layer and the classification is performed by a fully connected layer [23].”
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Niles, D. N., Amaguaña, D. A., Lojan, A. B., Salum, G. M., Villalba-Meneses, F., Tirado-Espín, A., Alvarado-Cando, O., Jaramillo, A. P. N., & Almeida-Galárraga, D. A. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-99170-8_27 · Cited by 7
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Brain Tumor Semantic Segmentation using Residual U-Net++ Encoder-Decoder Architecture
Mokhtar, M., Abdel-Galil, H., & Khoriba, G. A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01406119 · Cited by 7
“[5] to design and develop three primitive operations that make search space that find two cell architectures, DownSC and UpSC, useful in medical image segmentation especially.”
BackgroundMethodInfluentialA Machine Learning-based Multi-Phase Medical Image Classification for Internet of Medical Things
Poornima, D., Shabu, S. L. J., Aswin, T. S., et al. (2023) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac58438.2023.10290628 · Cited by 7
“Weng et al. (2019) proposed a neural architecture search-based approach for medical image segmentation [12].”
BackgroundMultiscale Fusion Network With SR-Attention for Seismic Velocity Model Building
Lu, J. J., Wu, C., Qu, Y., Zhang, H., & Ma, B. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3329932 · Cited by 7
“Since then, Unet has been widely worked in various directions of semantic segmentation (e.g., satellite image segmentation [27], [28], [29], industrial defect detection [30], [31], [32], and object detection [33], [34], [35]) due to its outstanding segmentation results.”
BackgroundPark, K.-M., Shin, D., & Yoo, Y. (2020) · Applied Sciences · doi:10.3390/app10103457 · Cited by 7
“NAS has three stages: search space, search strategy, and performance estimation strategy [1,31].”
BackgroundNeural Architecture Search for Microscopy Cell Segmentation
Zhu, Y., & Meijering, E. H. W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_55 · Cited by 7
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image SegmentationPreprint
Baldeon-Calisto, M., & Lai-Yuen, S. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11587 · Cited by 8
“In [23], Weng et al. proposed three types of primitive operation sets to construct down-sampling and up-sampling cells for a 2D U-Net backbone network where the cell configurations are updated using the DARTS [18] differential search strategy.”
MethodU-Net Based Architecture for an Improved Multiresolution Segmentation in Medical ImagesPreprint
Jahangard, S., Zangooei, M. H., & Shahedi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08238 · Cited by 8
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Artificial Intelligence-Assisted Microfluidic Bio-Imaging—From Sensors to Applications: A Review
Chen, J., Fu, L. P., Xiang, R., Luo, S., Huang, X., Yu, H., & Sun, L. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3507081 · Cited by 8
“Currently, deep learning-based segmentation methods, such as U-Net [93] and RefineNet [94] are widely used due to their excellent segmentation performance.”
Ibarra, J. P. G., Cuevas, F. J., & Vidales, J. R. H. (2025) · Horticulturae · doi:10.3390/horticulturae11050514 · Cited by 10
“Weng [8] used UNet in combination with a NAS algorithm for the segmentation of elements in magnetic resonance imaging, computed tomography and others.”
Chen, Y., Bian, S., Liu, Y., et al. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101791 · Cited by 6
SegQNAS: Quantum-inspired Neural Architecture Search applied to Medical Image Semantic Segmentation
Carlos, G., Figueiredo, K., Hussain, A. J., et al. (2023) · doi:10.1109/ijcnn54540.2023.10191869 · Cited by 6
“NAS-unet [21] searches for repeated downscaling and upscaling cells having a cell search space with a fixed U-Net network template.”
Object segmentation for image indexing in large database
Sikder, J., Islam, M. K., & Jahan, F. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.101937 · Cited by 6
“In this way, the U-Net framework extracts the features from the image and masks which assist the segmentation stage to identify objects and non-object from an input image (Weng et al., 2019).”
HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management
Miao, A. J., Lin, S., Lu, J., et al. (2024) · International Symposium on Medical Robotics · doi:10.1109/ismr63436.2024.10585699 · Cited by 6
“Existing methods include U-Net-based models [16], [17], [18], [19] which have shown exemplary performance in a wide variety of tasks [20] with little training data.”
MethodParsa, M., Schuman, C. D., Rathi, N., et al. (2021) · International Conference on Neuromorphic Systems (ICONS) · doi:10.1145/3477145.3477160 · Cited by 6
“The objective of these techniques is to automate architectural engineering to discover a network design which provides maximum performance [4, 11, 23, 29, 32].”
MethodSegmentation of Polyp Instruments using UNet based deep learning model
Mirza, A., & Rajak, R. K. (2021) · Nordic Machine Intelligence · doi:10.5617/nmi.9145 · Cited by 6
U-Net Semantic Segmentation of Digital Maps Using Google Satellite Images
Nguyen-Khanh, L., Nguyen-Ngoc-Yen, V., & Dinh-Quoc, H. (2021) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics54270.2021.9701566 · Cited by 6
Neural architecture search for medical image applications
Vo-Ho, V.-K., Yamazaki, K., Hoang, H., Tran, M., & Le, N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-399851-2.00029-6 · Cited by 10
Neural Architecture Search Using Metaheuristics for Automated Cell Segmentation
Kuş, Z., Aydın, M., Kıraz, B., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26504-4_12 · Cited by 6
MRF-UNets: Searching UNet with Markov Random Fields
Wang, Z., & Blaschko, M. B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26409-2_36 · Cited by 7
Li, Y., Baik, J., Rahman, M. M., et al. (2023) · doi:10.1145/3624062.3624258 · Cited by 6
“…for NAS are the search space definition (i.e., all model architectures to be possibly explored), a search algorithm (i.e., how to select model architectures, such as, gradient-based methods [36]), and performance estimation (i.e., evaluating the performance of the selected model architectures).”
BackgroundSelf-citationCao, C., Huang, W., Hu, F., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107774 · Cited by 6
Jin, X., Gu, S., Rao, Y., et al. (2024) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2023.105963 · Cited by 6
Evolutionary Attention Network for Medical Image Segmentation
Hassanzadeh, T., Essam, D., & Sarker, R. A. (2020) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta51227.2020.9363425 · Cited by 6
“NAS U–Net [54] is an automatic designed network using Reinforcement (a) Prostate Dataset”
MethodMixSearch: Searching for Domain Generalized Medical Image Segmentation ArchitecturesPreprint
Liu, L., Wen, Z., Liu, S., Zhou, H.-Y., Zhu, H., Xie, W., Shen, L., Ma, K., & Zheng, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.13280 · Cited by 8
“In order to further show the effectiveness of our proposed method, we take NAS-Unet [33] as an example and extend our method on it to show the improved generalization ability of learned architectures.”
MethodAS-3DFCN: Automatically Seeking 3DFCN-Based Brain Tumor Segmentation
Liu, R., Nan, H., Zou, Y., et al. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10168-x · Cited by 5
“[15] Search for the operator with highest weight 2D segmentation Mortazi et al. [16] Maximizing the metrics for exhaustive method 2D segmentation Ours Genetic algorithm search for topology structure 3D segmentation Table 2 Comparison with other brain tumor segmentation methods”
MethodLópez-Varela, E., de Moura, J., Novo, J., et al. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111304 · Cited by 5
Potocek, P., Ozsoy-Keskinbora, C., Müller, P., et al. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300745 · Cited by 5
Neural architecture search for image super-resolution: A review on the emerging state-of-the-art
García, J. L. L., Monroy, R., & Hernández, V. A. S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128481 · Cited by 5
Bargagna, F., Zigrino, D., Santi, L. A. D., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01275-8 · Cited by 5
“This novel methodology has been used to accomplish some medical tasks, such as classifying skin lesions [8] or segmenting medical images for surgery planning and computer-aided diagnosis [9].”
InfluentialHybrid 3D Medical Image Segmentation Using CNN and Frequency Transformer Fusion
Labbihi, I., Meslouhi, O. E., Elassad, Z. E. A., et al. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09602-5 · Cited by 5
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Improved U-Net model for remote sensing image classification method based on distributed storage
Jing, W., Zhang, M., & Tian, D. (2020) · Journal of Real-Time Image Processing · doi:10.1007/s11554-020-01028-0 · Cited by 5
“Wang et al. [20] developed a two-stage modified Unet framework to carry out multi-class segmentation on full resolution image Weng et al. [21] applied Neural Architecture Search to U-Net and realized the automatic design of Decoder.”
MethodProbeable DARTS with Application to Computational Pathology
Tang, S., Hosseini, M. S., Chen, L., Varma, S., Rowsell, C. H., Damaskinos, S., Plataniotis, K. N., & Wang, Z. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00070 · Cited by 5
“In medical image analysis, it has been utilized to find suitable networks for various applications, such as image segmentation for Magnetic Resonance Imaging (MRI) [17, 35, 3], ultrasound imaging [34], disease diagnoses from Computed Tomography (CT) scans [16, 9], etc.”
BackgroundMethodImproved Prostate Biparameter Magnetic Resonance Image Segmentation Based on Def-UNet
Huang, X., Pang, B., Zhang, T., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3268576 · Cited by 5
“In addition, U-net uses the skip connection operation to connect each pair of the down-sampling layer and the up-sampling layer, which makes the spatial information directly applied to much deeper layers and a more accurate segmentation result [40].”
MethodSelf-citationCombining frequency transformer and CNNs for medical image segmentation
Labbihi, I., Meslouhi, O. E., Benaddy, M., Kardouchi, M., & Akhloufi, M. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16279-9 · Cited by 5
Son, S., Joo, B., Park, M., Suh, S. H., Oh, H. S., Kim, J. W., Lee, S.-Y., Ahn, S. J., & Lee, J. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2023.1273013 · Cited by 5
Li, G., Cao, C., Fu, H., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3447010 · Cited by 5
A Segmentation Method of Lung Parenchyma From Chest CT Images Based on Dual U-Net
Tan, W., Liu, Y., Liu, H., Yang, J., Yin, X., & Zhang, Y. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983381 · Cited by 5
“[9]presented a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs), tailored to glioblastomas pictured in MR images.”
MethodPyramidal Nonlocal Network for Histopathological Image of Breast Lymph Node Segmentation
Bozdağ, Z., & Talu, M. F. (2020) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.201030.001 · Cited by 5
“Segmentation networks have been formed based on these networks [17–22].”
MethodF-UNet: A Modified U-Net Architecture for Segmentation of Stroke Lesion
Basak, H., & Rana, A. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-1086-8_4 · Cited by 5
Brain Tumor Segmentation Using Neural Network Topology Search
Milesi, A., Futrega, M., Marcinkiewicz, M., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_31 · Cited by 5
Medical Image Segmentation Using Grey Wolf-Based U-Net with Bi-Directional Convolutional LSTM
Tamilmani, G., Chintalapati, P. V., Devi, V. B., et al. (2023) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001423540253 · Cited by 5
Towards cardiac intervention assistance
Zeng, D., Jiang, W., Wang, T., et al. (2020) · doi:10.1145/3400302.3415789 · Cited by 5
“development of NAS for semantic image segmentation tasks [6, 22], especially in the field of medical image segmentation [8, 20, 27, 33, 38].”
BackgroundMethodInfluentialTimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time SeriesPreprint
Jiao, Y., Yang, K., Dou, S., Luo, P., Liu, S., & Song, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01596 · Cited by 9
“, semantic segmentation (Weng et al., 2019; Nekrasov et al., 2019; Bae et al., 2019), translation (Fan et al., 2020) and sequence labeling (Chen et al., 2018a).”
BackgroundAl-Yaari, A., Deng, Y., Abdullahi, M., & Shakeel, M. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae364b · Cited by 4
Deng, L., Zhou, Q., Wang, S., & Zhang, Y. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300185 · Cited by 4
Deepak Kumar, K., Senthil Pandi, S., Kumar, P., et al. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895268 · Cited by 4
“Weng et al. [12] employed NAS in medical image segmentation and in the NAS-Unet model, two supporting cells which are down sampling (Down SC) and the up. sampling (UpSC) cells improve the model.”
Thinking and Prospect of Power Chip Specificity
Ma, F., Li, M., Dong, X., Wang, B., Zhou, Y., Li, J., Feng, L., & Mohamed, M. A. (2021) · International Journal of Photoenergy · doi:10.1155/2021/1512629 · Cited by 4
“In recent years, the researches on network architecture search (NAS) have mainly focused on the definition of NAS search space, NAS search algorithms, and evaluation of model performance [37].”
BackgroundMachine learning analysis of self-assembled colloidal cones
Doan, D., Echeveste, D. J., Kulikowski, J., et al. (2022) · Soft Matter · doi:10.1039/d1sm01466h · Cited by 4
Ye, G., & Kaya, M. (2022) · Bioengineering · doi:10.3390/bioengineering9020081 · Cited by 4
Thomaz, A. A. S., Lima, J. A. S., Miosso, C. J., et al. (2022) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist55454.2022.9827727 · Cited by 4
Semantic segmentation of unmanned aerial vehicle image based on Resnet-Unet
Li, Z., Liu, W., Wu, G., et al. (2023) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms60103.2023.10347663 · Cited by 4
“Compared to the FCN model, the Unet model offers advantages such as a simple structure, shorter training time, and fewer training parameters[10,11].”
MethodAttention Gated Multi-Axial Fusion Network for Brain MRI Lesion Segmentation
Islam, M. T., Taki, A. A., & Fattah, S. A. (2024) · doi:10.1109/iceeict62016.2024.10534354 · Cited by 4
“U-Net based architectures have shown good performance on various medical image segmentation tasks including stroke lesion segmentation [18].”
DIPO: Differentiable Parallel Operation Blocks for Surgical Neural Architecture Search
Lee, M., Sánchez-Matilla, R., Stoyanov, D. V., & Luengo, I. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3406065 · Cited by 5
“We include NAS-Unet [13] for comparison as another U-Net based NAS method.”
NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search
Wang, W., & Li, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107022 · Cited by 4
Tao, Q., Xiao, Y., Hu, L., et al. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2403630 · Cited by 4
“U-Net, as a network architecture widely applied in image classification and segmentation tasks, is adept at finely predicting the category of each pixel (Guan et al. 2019; Huang et al. 2020; Weng et al. 2019).”
Modern Thyroid Cancer Diagnosis: A Review of AI-Powered Algorithms for Detection and Classification
Boruah, K., Dutta, L., & Pathak, M. K. (2024) · INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING · doi:10.47164/ijngc.v15i3.1768 · Cited by 4
Peng, Y., Zhang, R., Hu, C., & Guo, B. (2025) · Electronics · doi:10.3390/electronics14081658 · Cited by 4
Chondro, P., Haq, Q. M. U., Ruan, S.-J., et al. (2020) · Mathematics · doi:10.3390/math8050768 · Cited by 4
“Currently, most prior arts focus on providing a segmentation algorithm using deep learning solution for CT images [12,14].”
BackgroundSequential Feature Filtering Classifier
Seo, M., Lee, J., Park, J., Kim, D., & Choi, D. (2021) · IEEE Access · doi:10.1109/access.2021.3090439 · Cited by 4
Design of Steel Surface Defect Detection System Based on deep Learning
Liu, M., Huang, Y., & Xie, Y. (2022) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica54878.2022.9844624 · Cited by 4
A Regional Multiple Instance Learning Network for Whole Slide Image Segmentation
Cai, H., Yi, W., Li, Y., Liao, W., & Song, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995017 · Cited by 4
“The convolutional u-shaped architecture is widely used in medical image segmentation [14]–[16].”
MethodSelf-citationAutomatic Retinal Vessel Segmentation Using BTLBO
Rajesh, C., & Kumar, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6525-8_15 · Cited by 4
Classification of Brain Tumors Using Ensemble Learning
Saranya, G., Victoria, H., Safa, M., et al. (2023) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt57126.2023.10134687 · Cited by 4
“For the entire tumor segmentation using the FLAIR modality, the authors employ the U-Net [22] model and encoder- decoder model, respectively, to get maximum F1 scores of 0.94 and 0.93.”
BackgroundMethodInfluentialNeural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingPreprint
Wang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06338 · Cited by 4
“When it comes to medical imaging segmentations, the NAS-Unet respectively looks for the optimal basic down-sampling and up-sampling units and constructs the U-Net shaped architectures, which has been tested on 2D medical image datasets including the prostate MRI, liver computed tomography (CT) and nerves ultrasound im…”
MethodSujini, G. N., & Balakrishna, S. (2026) · Scientific Reports · doi:10.1038/s41598-025-34731-8 · Cited by 3
“NAS has shown significant performance in medical image segmentation, as evidenced by studies such as Mortazi et al. 40 and Weng et al. 43 , which optimize building block structures and repetitively stack them to construct architectures.”
Benedykciuk, E., Denkowski, M., & Wójcik, G. M. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102713 · Cited by 4
SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation
Chen, R., Luo, J., Nian, F., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446167 · Cited by 3
“…43.5M 0.909 ± 0.006 0 .839 ± 0.009 Transfuse [26] 26.3M 0.923 ± 0.004 0 .861 ± 0.006 PSPNet [5] 49.0M 0.916 ± 0.005 0 .850 ± 0.008 Pure NAS NasUNet [14] 0.8M 0.914 ± 0.004 0 .845 ± 0.005 Auto-DeepLab [15] 44.4M 0.918 ± 0.003 0 .851 ± 0.006 MixSearch [20] 10.4M 0.917 ± 0.004 0 .852 ± 0.004 Hybrid…”
MethodInfluential
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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