Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 145-150.doi: 10.11896/jsjkx.191100098

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Survey of Classification Methods of Breast Cancer Histopathological Images

MAN Rui1, YANG Ping1, JI Cheng-yu1, XU Bo-wen2   

  1. 1 Smart City College,Beijing Union University,Beijing 100101,China
    2 Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:MAN Rui,born in 1996,postgraduate.Her main research interests include medicalimage processing and so on.
    YANG Ping,born in 1973,associate professor,master supervisor.Her main research interests include signal and information processing.

PDF (PC)

2231

Abstract: Histopathological examination of breast cancer is the “gold standard” for breast cancer diagnosis.The classification of breast cancer histopathological images has become a hot research topic in the field of medical image processing.The accurate classification of images has great application value in the fields of assisting doctors to diagnose the disease and meeting the needs of clinical application.This paper assesses the advantages and disadvantages of one breast cancer histopathological image classification algorithm.The methods are classified into two categories,depending on whether or not it is necessary to manually extract feature of breast cancer histopathological images or if the classification of breast cancer histopathological images can be based on a deep learning algorithm.The research on binary or multi-classification of breast cancer histopathology images is further tracked.Finally,the classification algorithm of breast cancer histopathology images using the latest theory of deep learning is gi-ven.Conclusions of the classification study of breast cancer histopathological images are drawn,and possible directions in the future are discussed.

Key words: Breast cancer, Deep learning, Feature extraction, Histopathological images, Image classification

CLC Number: 

  • TP3-05
[1] WANG S,LIU J,BI Y Y,et al.Automatic Recognition of Breast Gland Based on Two-step Clustering and Random Forest [J].Computer Science,2018,45(3):249-254.
[2] BISWAS M,KUPPILI V,SABA L,et al.State-of-the-art review on deep learning in medical imaging[J].Frontiers in bioscience (Landmark edition),2019,24:392-426.
[3] ROBERTSON S,AZIZPOUR H,SMITH K,et al.Digital image analysis in breast pathology-from image processing techniques to artificial intelligence [J].Translational Research,2018(194):19-35.
[4] XU J,XIANG L,LIU Q,et al.Stacked sparse autoencoder(SSAE) for nuclei detection on breast cancer histopathology images[J].IEEE Transactions on Medical Imaging,2015,35(1):119-130.
[5] HARALICK R M,SHANMUGAMK,DINSTEIN I.TexturalFeatures for Image Classification[J].Studies in Media and Communication,1973,SMC-3(6):610-621.
[6] OJALA T,PIETIKINEN M,HARWOOD D.A ComparativeStudy of Texture Measures with Classification based on Feature Distribution[J].Pattern Recognition,1996,29:51-59.
[7] OJALA T,PIETIKINEN M,MAENPAA T.MultiresolutionGray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[J].Pattern analysis and Machine Intelligence,2002,24(7):971-986.
[8] GUO Z,ZHANG L,ZHANG D.A Completed Modeling of Local Binary Pattern Operator for Texture Classification[J].IEEE Transactions on Image Processing,2010,19(6):1657-1663.
[9] KOWAL M,FILIPCZUK P,OBUCHOWICZ A,et al.Computer aided diagnosis of breast cancer based on fine needle biopsy microscopic images[J].Computers in Biology and Medicine,2013,43(10):1563-1572.
[10] FILIPCZUK P,FEVENS T,KRZYZAK A,et al.Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies[J].IEEE Transactions on Medical Imaging,2013,32(12):2169-2178.
[11] GEORGEY M,ZAYED H H,ROUSHDY M I,et al.Remotecomputer-aided breast cancer detection and diagnosis system based on cytological images[J].IEEE Systems Journal,2013,8(3):949-964.
[12] WANG P,HU X,LI Y,et al.Automatic cell nuclei segmentation and classification of breast cancer histopathology images [J].Signal Processing,2016,122:1-13.
[13] OSAREH A,SHADGAR B.Machine learning techniques todiagnose breast cancer[C]//2010 5th International Symposium on Health Informatics and Bioinformatics.IEEE,2010:114-120.
[14] SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.A Dataset for Breast Cancer Histopathological Image Classification[J].IEEE transactions on biomedical engineering,2015,63(7):1455-1462.
[15] SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.Breast Cancer Histopathological Image Classification using Convolutional Neural Networks[C]//2016 International Joint Confe-rence on Neural Networks (IJCNN).New York:IEEE Press,2016:2560-2567.
[16] BAYRAMOGLU N,KANNALA J,HEIKKILÄ J.Deep learning for magnification independent breast cancer histopathology image classification[C]//2016 23rd International Conference on Pattern Recognition (ICPR).New York:IEEE Press,2016:2440-2445.
[17] CIMPOI M,MAJI S,VEDALDI A.Deep filter banks for texture recognition and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015:3828-3836.
[18] SONG Y,ZOU J J,CHANG H,et al.Adapting fisher vectorsfor histopathology image classification[C]//2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).IEEE,2017:600-603.
[19] SONG Y,CHANG H,HUANG H,et al.Supervised intra-embedding of fisher vectors for histopathology image classification[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2017:99-106.
[20] WEI B Z,HAN Z Y,HE X Y,et al.Deep learning model based breast cancer histopathology image clasification[C]//2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).IEEE,2017:348-353.
[21] WANG D,KHOSLA A,GARGRYA R,et al.Deep learning for identifying metastatic breast cancer[J].arXiv:1606.05718,2016.
[22] NAHID A A,MEHRABI M A,KONG Y.Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification[C]//2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).IEEE,2017:410-415.
[23] ARAÚJO T,ARESTA G,CASTRO E,et al.Classification of breast cancer histology images using convolutional neural networks[J].PloS One,2017,12(6):e0177544.
[24] HAN Z,WEI B,ZHENG Y,et al.Breast cancer multi-classification from histopathological images with structured deep learning model[J].Scientific Reports,2017,7(1):4172.
[25] BARDOU D,ZHANG K,AHMAD S M.Classification of breast cancer based on histology images using convolutional neural networks[J].IEEE Access,2018,6:24680-24693.
[26] SUDHARSHAN P J,PETITJEAN C,SPANHOL F,et al.Multiple instance learning for histopathological breast cancer image classification[J].Expert Systems with Applications,2019,117:103-111.
[27] SUN M,HAN T X,LIU M C,et al.Multiple instance learning convolutional neural networks for object recognition[C]//2016 23rd International Conference on Pattern Recognition (ICPR).New York:IEEE Press,2016:3270-3275.
[28] VENKATESAN R,CHANDAKKAR P,LI B.Simpler non-parametric methods provide as good or better results to multiple-instance learning[C]//Proceedings of the IEEE International Conference on Computer Vision.New York:IEEE Press,2015:2605-2613.
[29] HUANG G,LIUZ,VAN DER MAATER L,et al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2017:4700-4708.
[30] GUPTA V,BHAVSAR A.Sequential modeling of deep features for breast cancer histopathological image classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.New York:IEEE Press,2018:2254-2261.
[31] LI Y,XIE X,SHEN L,et al.Reverse active learning based atrousDenseNet for pathological image classification[J].BMC Bioinformatics,2019,20(1):445.
[32] PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on knowledge and data engineering,2009,22(10):1345-1359.
[33] SPANHOL F A,OLIVEIRA L S,CAVALIN P R,et al.Deep features for breast cancer histopathological image classification[C]//2017 IEEE International Conference on Systems,Man,and Cybernetics (SMC).New York:IEEE Press,2017:1868-1873.
[34] HE X Y,HAN Z Y,WEI B Z.Automatic classification of histopa-thology images of breast cancer based on deep learning [J].Computer Engineering and Applications,2018,54(12):126-130.
[35] AHMAD H M,GHUFFAR S,KHURSHID K.Classification of Breast Cancer Histology Images Using Transfer Learning[C]//2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST).New York:IEEE Press,2019:328-332.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[3] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[5] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[8] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[10] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[11] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[12] ZHANG Yuan, KANG Le, GONG Zhao-hui, ZHANG Zhi-hong. Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM [J]. Computer Science, 2022, 49(7): 31-39.
[13] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[14] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[15] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.