计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 250-256.doi: 10.11896/jsjkx.220600008
田天祎, 孙福明
TIAN Tian-yi, SUN Fu-ming
摘要： 关注人的生命健康,定期进行癌症筛查是一项极为重要的工作。针对肿瘤图像数据集数量较少且存在部分无标签的问题,提出了一种基于域自适应算法的肿瘤识别模型。其主干网络包括特征提取器、标签分类器和域判别器。其中,特征提取器对源域和目标域的特征进行提取,学习肿瘤特征;标签分类器对肿瘤图像进行分类输出;域判别器对数据特征的来源进行判定。标签分类器与域判别器博弈,获取源域和目标域的数据分布,直到二者在特征空间上的分布趋于一致,此时得到的分类器可对目标域的数据进行分类。在BreakHis数据集上的实验结果表明,所提算法的平均准确率达到了87.6%,与两种经典域自适应方法相比,其准确率分别提高了16.2%和14.1%,并且在无标签的数据集上显示出了良好的性能。
中图分类号:
| [1]ZHENG G Y,LIU X B,HAN G H.Review of computer-aided detection and diagnosis systems for medical imaging[J].Joural of Software,2018,29(5):1471-1514. [2]SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.Breast cancer histopathological image classification using convolutional neural networks[C]//International Joint Conference on Neural Networks.2016:2560-2567. [3]BAYRAMOGLU N,KANNALA J,HEIKKILÄ J.Deep lear-ning for magnification independent breast cancer histopathology image classification[C]//International Conference on Pattern Recognition.2016:2440-2445. [4]FARAHANI A,VOGHOEI S,RASHEED K,et al.A Brief Review of Domain Adaptation [J].arXiv:2010.03978,2020. [5]LONG M S.Transfer Learning Problems and Methods [D].Beijing:Tsinghua University,2014. [6]LONG M S,ZHU H,WANG J,et al.Deep Transfer Learning with Joint Adaptation Networks [C]//International Conference on Machine Learning.2017:2208-2217. [7]ZHANG Y,LIU T,LONG M,et al.Bridging Theory and Algorithm for Domain Adaptation[C]//International Conference on Machine Learning.2019:7404-7413. [8]TZENG E,HOFFMAN J,ZHANG N,et al.Deep Domain Confusion:Maximizing for Domain Invariance[J].arXiv:1412.3474,2014. [9]LONG M,CAO Y,WANG J,et al.Learning Transferable Features with Deep Adaptation Networks [C]//International Conference on Machine Learning.2015:97-105. [10]ZHUO J,WANG S,ZHANG W,et al.Deep Unsupervised Convolutional Domain Adaptation[C]//Proceedings of the 25th ACM International Conference on Multimedia.2017:261-269. [11]GHIFARY M,KLEIJN W B,ZHANG M,et al.Domain Gene-ralization for Object Recognition with Multi-task Autoencoders[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:2551-2559. [12]BOUSMALIS K,SILBERMAN N,DOHAN D,et al.Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:3722-3731. [13]HOFFMAN J,TZENG E,PARK T,et al.Cycada:Cycle-consistent adversarial domain adaptation[C]//International Confe-rence on Machine Learning.2018:1989-1998. [14]ZHU J Y,PARK T,ISOLA P,et al.Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2223-2232. [15]YUE X,ZHENG W,ZHANG S,et al.Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:13834-13844. [16]GUAN H,LIU M.Domain Adaptation for Medical Image Ana-lysis:A Survey [J].IEEE Transactions on Biomedical Enginee-ring,2022,69(3):1173-1185. [17]ZHANG Y,TANG H,JIA H,et al.Domain-Symmetric Net-works for Adversarial Domain Adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:5031-5040. |
| [1] | 郑文萍, 刘美麟, 杨贵. 一种基于节点稳定性和邻域相似性的社区发现算法 Community Detection Algorithm Based on Node Stability and Neighbor Similarity 计算机科学, 2022, 49(9): 83-91. https://doi.org/10.11896/jsjkx.220400146 |
| [2] | 胡安祥, 尹小康, 朱肖雅, 刘胜利. 基于数据流特征的比较类函数识别方法 Strcmp-like Function Identification Method Based on Data Flow Feature Matching 计算机科学, 2022, 49(9): 326-332. https://doi.org/10.11896/jsjkx.220200163 |
| [3] | 武红鑫, 韩萌, 陈志强, 张喜龙, 李慕航. 监督和半监督学习下的多标签分类综述 Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning 计算机科学, 2022, 49(8): 12-25. https://doi.org/10.11896/jsjkx.210700111 |
| [4] | 李斌, 万源. 基于相似度矩阵学习和矩阵校正的无监督多视角特征选择 Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment 计算机科学, 2022, 49(8): 86-96. https://doi.org/10.11896/jsjkx.210700124 |
| [5] | 陈晶, 吴玲玲. 多源异构环境下的车联网大数据混合属性特征检测方法 Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment 计算机科学, 2022, 49(8): 108-112. https://doi.org/10.11896/jsjkx.220300273 |
| [6] | 刘冬梅, 徐洋, 吴泽彬, 刘倩, 宋斌, 韦志辉. 基于边框距离度量的增量目标检测方法 Incremental Object Detection Method Based on Border Distance Measurement 计算机科学, 2022, 49(8): 136-142. https://doi.org/10.11896/jsjkx.220100132 |
| [7] | 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094 |
| [8] | 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 |
| [9] | 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 |
| [10] | 帅剑波, 王金策, 黄飞虎, 彭舰. 基于神经架构搜索的点击率预测模型 Click-Through Rate Prediction Model Based on Neural Architecture Search 计算机科学, 2022, 49(7): 10-17. https://doi.org/10.11896/jsjkx.210600009 |
| [11] | 张源, 康乐, 宫朝辉, 张志鸿. 基于Bi-LSTM的期货市场关联交易行为检测方法 Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM 计算机科学, 2022, 49(7): 31-39. https://doi.org/10.11896/jsjkx.210400304 |
| [12] | 高振卓, 王志海, 刘海洋. 嵌入典型时间序列特征的随机Shapelet森林算法 Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features 计算机科学, 2022, 49(7): 40-49. https://doi.org/10.11896/jsjkx.210700226 |
| [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] | 曾志贤, 曹建军, 翁年凤, 蒋国权, 徐滨. 基于注意力机制的细粒度语义关联视频-文本跨模态实体分辨 Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism 计算机科学, 2022, 49(7): 106-112. https://doi.org/10.11896/jsjkx.210500224 |
|
||
首页