计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 67-73.doi: 10.11896/jsjkx.201000188
和青芳, 王慧, 程光
HE Qing-fang, WANG Hui, CHENG Guang
摘要: 针对乳腺癌病理组织图像数据普遍存在数据集规模小、良性和恶性样本数量分布不均衡、自动识别精度低的现状,利用深度可分离卷积、小卷积核堆叠、增深降维等技术,结合文中提出的“SoftMax+WF”设计具备合理深度和宽度、适应小数据集、轻型的病理组织图像分类模型。在图像旋转、扭曲等传统增强数据方法基础上,采用随机不重复裁切法均衡良、恶性样本数量并扩充数据集。针对训练集中难以聚类的样本,提出“弱特征”概念、“弱特征”样本提取算法和自适应调整、二次训练算法改进模型训练。在参数设置和运行环境相同的条件下,进行8组比对实验,模型的准确率、敏感度、特异度均可达97%以上。实验结果证明文中设计的模型性能稳定,对小数据集和不均衡数据集具有较好的包容性和适应性。
中图分类号:
[1] BRAY F,FERLAY J,SOERJOMATARAM I,et al.Globalcancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA Cancer J Clin,2018,68(6):394-424. [2] ZHENG R S,SUN K X,ZHANG S W,et al.Analysis of the prevalence of malignant tumors in China in 2015[J].Chinese Journal of Oncology,2019,41(1):19-28. [3] LV F,YANG S L.Analysis of pathological diagnosis of frozen sections of breast cancer [J].Chinese Community Physician,2012,14 (3):224-225. [4] ZENG Z H.Study on pathological and clinical factors of diagnosis accuracy of frozen section of breast malignant tumor [J].Chinese Medical Guide,2012,10(10):29-30. [5] HE X Y,HAN Z Y,ZHENG W B.Automatic classification of breast cancer pathological images based on deep learning[J].Computer Engineering and Applications,2018,54 (12):121-125. [6] WANG M,LIU B,FOROOSH H.Factorized convolutional neural networks[J].arXiv:1608.04337,2016. [7] CHOLLET F.Xception:Deep Learning with Depthwise Separable Convolutions[J].arXiv:1610.02357v3,2017. [8] KOWAL M,FILIPCZUK P,OBUCHOWICZ A,et al.Compu-ter-aided diagnosis of breast cancer based on fine needle biopsy microscopic images[J].Computers in Biology and Medicine,2013,43(10):1563-1572. [9] TIMMANA H K,AJABHUSHNAM C.Bosom Malignant Di-seases (Cancer) Identification by using Deep Learning Technique[C]//2019 Third International conference on I-SMAC (IoT in Social Mobile Analytics and Cloud) (I-SMAC).2019:1-7. [10] SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.A dataset for breast cancer histopathological image classification[J].IEEE Transactions on Biomedical Engineering,2016,63(7):1455-1462. [11] BAYRAMOGLU N,KANNALA J,HEIKKILÄ J.Deep learning for magnification independent breast cancer histopathology image classification[C]//International Conference on Pattern Recognition(ICPR).2016:2441-2446. [12] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations.2015:1-14. [13] BENGIO Y,DELALLEAU O.On the expressive power of deep architectures[C]//International Conference on Algorithmic Learning Theory.Berlin Heidelberg:Springer,2011:18-36. [14] CHOLLET F.Xception:Deep learning with depthwise separable convolutions[J].arXiv:1610.02357,2017. [15] LI L,YIN S C.Realization of FPGA-based Softmax layer of convolutional neural network [J].Modern Computer (Professional Edition),2017(26):21-24. [16] GOODFELLOW I,ENGIO Y,COURVILLE A.Deep learning[M].Mit Press,2017:306-309. [17] HE K M.Identity Mappings in Deep Residual Networks[J].arXiv:1603.05027,2016. [18] IOFFE S,SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv:1502.03167,2015. [19] HOWARD A G,ZHU M L,CHEN B.MobileNets:EfficientConvolutional Neural Networks for Mobile Vision Applications[J].arXiv:1704.04861,2017. [20] WARDEN P.Why you need to improve your training data,and how to do it[OL].[2018-05-28].https://petewarden.com/2018/05/28/why-you-need-to-improve-your-training-data-and-how-to-do-it/. [21] KINGMA D,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014. [22] Editorial Department of Journal of Practical Medicine.The clinical significance of sensitivity and specificity [J].Journal of Practical Medicine,2000,16(2):904-904. [23] SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.Breast cancer histopathological image classification using Convolutional Neural Networks[C]//2016 International Joint Conference on Neural Networks(IJCNN).2016:2560-2567. [24] HE X Y,HAN Z Y,WEI B Z.Automatic classification of pathological images of breast cancer based on deep learning[J].Computer Engineering and Applications,2018,54(12):121-125. [25] WANG H,LI X,SHEN Q,et al.Research on breast cancerpathological image classification based on AutoAugment and residual network[J].Journal of China Jiliang University,2019,30(3):343-350. [26] RAJPURKAR P,HANNUN A Y,HAGHPANAHI M,et al.Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks[J].Computer Science,2017,225(5):308-325. [27] WANG W D,WANG R Z,WEI X L,et al.Automatic electrocardiogram recognition algorithm based on stacked two-way LSTM [J].Computer Science,2020,47 (7):118-124. [28] FENG Y Q,ZHANG L,MO J.Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2020,17(1):91-101. |
[1] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[2] | 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 |
[3] | 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 |
[4] | 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 |
[5] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
[6] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
[7] | 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 |
[8] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
[9] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
[10] | 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 |
[11] | 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 |
[12] | 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 |
[13] | 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 |
[14] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
[15] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
|