Academic Journal
Neural Architecture Search for Light-weight Medical Image Segmentation Network
| العنوان: | Neural Architecture Search for Light-weight Medical Image Segmentation Network |
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| المؤلفون: | ZHANG Fu-chang, ZHONG Guo-qiang, MAO Yu-xu |
| المصدر: | Jisuanji kexue, Vol 49, Iss 10, Pp 183-190 (2022) |
| بيانات النشر: | Editorial office of Computer Science, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer software LCC:Technology (General) |
| مصطلحات موضوعية: | deep learning, differentiable neural architecture search, light-weight convolutional neural networks, automatic network architecture design, medical image segmentation, Computer software, QA76.75-76.765, Technology (General), T1-995 |
| الوصف: | Most of the existing medical image segmentation models with excellent performance are manually designed by domain experts.The design process usually requires a lot of professional knowledge and repeated experiments.In addition,the over complex segmentation model not only has high requirements for hardware resources,but also has low segmentation efficiency.An neural architecture search method named Auto-LW-MISN(Automatically Light-weight Medical Image Segmentation Network) is proposed for automatic construction of light-weight medical image segmentation network.In this paper,by constructing a light-weight search space,designing a search super network for medical image segmentation,and designing a differentiable search stra-tegy with complexity constraints,a neural architecture search framework for automatic search of light-weight medical image segmentation network is established.Experimental results on microscope cell images,liver CT images and prostate MR images show that Auto-LW-MISN can automatically construct light-weight segmentation models for different modes of medical images,and its segmentation accuracy is improved compared with U-net,Attention U-net,Unet+＋and NAS-Unet. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | Chinese |
| تدمد: | 1002-137X |
| Relation: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-10-183.pdf; https://doaj.org/toc/1002-137X |
| DOI: | 10.11896/jsjkx.210800052 |
| URL الوصول: | https://doaj.org/article/79216f0998704f80934a68b468ba3584 |
| رقم الانضمام: | edsdoj.79216f0998704f80934a68b468ba3584 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 1002137X |
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| DOI: | 10.11896/jsjkx.210800052 |