计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 183-190.doi: 10.11896/jsjkx.210800052
张福昌, 仲国强, 毛玉旭
摘要: 现有的性能优异的医学图像分割模型大都由领域专家手动设计,设计过程往往需要大量专业知识和反复实验。此外,过度复杂的分割模型不仅对硬件资源有较高要求,且分割效率较低。为此,提出了用于自动构建轻量化医学图像分割网络的神经结构搜索方法Auto-LW-MISN(Automatically Light-Weight Medical Image Segmentation Network)。通过构建轻量级搜索空间、设计适用于医学图像分割的搜索超网络、设计添加复杂性约束的可微分搜索策略,建立用于自动搜索轻量化医学图像分割网络的神经结构搜索框架。在显微镜细胞图像、肝脏CT图像和前列腺MR图像等数据集上进行实验,结果表明,Auto-LW-MISN能够针对不同模态的医学图像自动构建轻量化的分割模型,其分割精度相比U-net,Attention U-net,Unet++和NAS-Unet等方法均有提高。
中图分类号:
[1]LIU C,XIAO Z Y,DU N M.Application of Improved Convolutional Neural Network in Medical Image Segmentation [J].Computer Science and Exploration,2019,13(9):1593-1603. [2]XU H,SUI L,ZHANG J W,et al.Research Progress of Convolutional Neural Network in Medical Image Segmentation [J].Chinese Journal of Medical Physics,2019,36(11):1302-1306. [3]RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Compu-ter-Assisted Intervention.Springer,2015:234-241. [4]KOREZ R,LIKAR B,PERNUS F,et al.Model-Based Segmentation of Vertebral Bodies from MR Images with 3D CNNs[C]//International Conference on Medical Image Computing and Computer-assisted Intervention.Springer,2016:433-441. [5]OKTAY O,SCHLEMPER J,FOLGOC L L,et al.Attention U-net:Learning Where to Look for the Pancreas[J].arXiv:1804.03999,2018. [6]HE K,ZHANG X,REN S,et al.Deep Residual Learning forImage Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:770-778. [7]ALOM M Z,HASAN M,YAKOPCIC C,et al.Recurrent Resi-dual Convolutional Neural Network based on U-net(R2U-net) for Medical Image Segmentation[J].arXiv:1802.06955,2018. [8]LIU H,SIMONYAN K,YANG Y.DARTS:Differentiable Architecture Search[J].arXiv:1806.09055,2018. [9]FANG J,SUN Y,ZHANG Q,et al.Densely Connected Search Space for More Flexible Neural Architecture Search[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2020:10625-10634. [10]LI G,QIAN G,DELGADILLO I C,et al.SGAS:SequentialGreedy Architecture Search[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2020:1617-1627. [11]WAN A,DAI X,ZHANG P,et al.FBNetV2:DifferentiableNeural Architecture Search for Spatial and Channel Dimensions[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2020:12962-12971. [12]DAI X,WAN A,ZHANG P,et al.FBNetV3:Joint Architecture-Recipe Search Using Predictor Pretraining[C]//IEEE Confe-rence on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2021:16276-16285. [13]WENG Y,ZHOU T,LI Y,et al.NAS-Unet:Neural Architecture Search for Medical Image Segmentation[J/OL].IEEE Access,2019,7:44247-44257.https://ieeexplore.ieee.org/document/8681706. [14]MORTAZI A,BAGCI U.Automatically Designing CNN Architectures for Medical Image Segmentation[C]//Machine Lear-ning in Medical Imaging-9th International Workshop.Sprin-ger,2018:98-106. [15]LI G,ZHANG X,WANG Z,et al.StacNAS:Towards Stable andConsistent Differentiable Neural Architecture Search[J].arXiv:1909.11926,2019. [16]YU F,KOLTUN V.Multi-scale Context Aggregation by Dilated Convolutions[J].arXiv:1511.07122,2015. [17]HAN K,WANG Y,TIAN Q,et al.GhostNet:More Features From Cheap Operations[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2020:1577-1586. [18]LI X,WANG W,HU X,et al.Selective Kernel Networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2019:510-519. [19]ZHANG X,ZHOU X,LIN M,et al.ShuffleNet:An Extremely Efficient Convolutional Neural Network for Mobile Devices[C]//IEEE Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE Computer Society,2018:6848-6856. [20]CAICEDO J,GOODMAN A,KARHOHS K,et al.Nucleus Segmentation Across Imaging Experiments:the 2018 Data Science Bowl[J].Nature Methods,2019,16(12):1247-1253. [21]DEVRIES T,TAYLOR G W.Improved Regularization of Con-volutional Neural Networks with Cutout[J].arXiv:1708.04552,2017. [22]ZHOU Z,SIDDIQUEE M M R,TAJBAKHSH N,et al.Unet++:A Nested U-net Architecture for Medical Image Segmentation[M]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Cham:Springer,2018:3-11. [23]KAVUR A E,SELVER M A,DICLE O,et al.Chaos-combined(CT-MR) Healthy Abdominal Organ Segmentation Challenge Data[C]//IEEE International Symposium on Biomedical Imaging(ISBI).Imag.(ISBI),2019. [24]LIU C,CHEN L C,SCHROFF F,et al.Auto-DeepLab:Hierarchical Neural Architecture Search for Semantic Image Segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Computer Vision Foundation/IEEE,2019:82-92. [25]LITJENS G,TOTH R,VAN DE VEN W,et al.Evaluation of Prostate Segmentation Algorithms for MRI:the PROMISE12 Challenge[J].Medical Image Analysis,2014,18(2):359-373. |
[1] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[2] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[4] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[5] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[7] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[8] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
[9] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
[10] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
[11] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[12] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
[13] | 王君锋, 刘凡, 杨赛, 吕坦悦, 陈峙宇, 许峰. 基于多源迁移学习的大坝裂缝检测 Dam Crack Detection Based on Multi-source Transfer Learning 计算机科学, 2022, 49(6A): 319-324. https://doi.org/10.11896/jsjkx.210500124 |
[14] | 楚玉春, 龚航, 王学芳, 刘培顺. 基于YOLOv4的目标检测知识蒸馏算法研究 Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 计算机科学, 2022, 49(6A): 337-344. https://doi.org/10.11896/jsjkx.210600204 |
[15] | 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测 Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121 |
|