Cross-task extreme learning machine for breast cancer image classification with deep convolutional features

Abstract Automatic classification of breast histopathology images plays a key role in computer-aided breast cancer diagnosis. However, feature-based classification methods rely on the accurate cell segmentation and feature extraction. Due to overlapping cells, dust, impurities and uneven irradiation the accurate segmentation and efficient feature extraction are still challenging. In order to overcome the above difficulties and limited breast histopathology images, in this paper, a hybrid structure which includes a double deep transfer learning (D2TL) and interactive cross-task extreme learning machine (ICELM) is proposed based on feature extraction and representation ability of CNN and classification robustness of ELM. First, high level features are extracted using deep transfer learning and double-step deep transfer learning. Then, the high level feature sets are jointly used as regularization terms to further improve classification performance in interactive cross task extreme learning machine. The proposed method was tested on 134 breast cancer histopathology images. Results show that our method has achieved remarkable performance in classification accuracy (96.67%, 96.96%, 98.18%). From the experiment result, the proposed method is promising for providing an efficient tool for breast cancer classification in clinical settings.

[1]  Alexander Rakhlin,et al.  Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis , 2018, bioRxiv.

[2]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[3]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Abdulhamit Subasi,et al.  Breast cancer diagnosis using GA feature selection and Rotation Forest , 2015, Neural Computing and Applications.

[5]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[6]  Qiang Yang,et al.  Translated Learning: Transfer Learning across Different Feature Spaces , 2008, NIPS.

[7]  Xu Liu,et al.  Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[8]  Hongming Xu,et al.  Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm , 2015, EURASIP Journal on Image and Video Processing.

[9]  R. Sivaramakrishna,et al.  Detection of breast cancer at a smaller size can reduce the likelihood of metastatic spread: a quantitative analysis. , 1997, Academic radiology.

[10]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  Jiri Matas,et al.  Systematic evaluation of convolution neural network advances on the Imagenet , 2017, Comput. Vis. Image Underst..

[12]  S. McGuire.  World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. , 2016, Advances in nutrition.

[13]  Yongming Li,et al.  Automatic cell nuclei segmentation and classification of breast cancer histopathology images , 2016, Signal Process..

[14]  David Zhang,et al.  Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems , 2015, IEEE Transactions on Instrumentation and Measurement.

[15]  Aymen Mouelhi,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automatic Image Segmentation of Nuclear Stained Breast Tissue Sections Using Color Active Contour Model and an Improved Watershed Method , 2022 .

[16]  Shu-Ching Chen,et al.  An efficient deep residual-inception network for multimedia classification , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[17]  Yan Liu,et al.  Common Subspace Learning via Cross-Domain Extreme Learning Machine , 2017, Cognitive Computation.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Benjamin Q. Huynh,et al.  SU-D-207B-06: Predicting Breast Cancer Malignancy On DCE-MRI Data Using Pre-Trained Convolutional Neural Networks. , 2016, Medical physics.

[20]  Mei-Ling Huang,et al.  Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis , 2010, Journal of Medical Systems.

[21]  Hariharan Ravishankar,et al.  Understanding the Mechanisms of Deep Transfer Learning for Medical Images , 2016, LABELS/DLMIA@MICCAI.

[22]  Mustafa Zuhaer Nayef Al-Dabagh,et al.  Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine , 2017 .

[23]  Pavel Kisilev,et al.  Medical Image Description Using Multi-task-loss CNN , 2016, LABELS/DLMIA@MICCAI.

[24]  Luiz Eduardo Soares de Oliveira,et al.  Breast cancer histopathological image classification using Convolutional Neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[25]  Kenli Li,et al.  An Ensemble CNN2ELM for Age Estimation , 2018, IEEE Transactions on Information Forensics and Security.

[26]  Sajid Hussain,et al.  Active contours for image segmentation using complex domain-based approach , 2016, IET Image Process..

[27]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[28]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[30]  Kyungtae Kang,et al.  Novel hybrid CNN-SVM model for recognition of functional magnetic resonance images , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[31]  Yuanjie Zheng,et al.  Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model , 2017, Scientific Reports.

[32]  Jonathan Le Roux,et al.  Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures , 2014, ArXiv.

[33]  Yan Liu,et al.  Deep object recognition across domains based on adaptive extreme learning machine , 2017, Neurocomputing.

[34]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[35]  Marek Kowal,et al.  Fuzzy Clustering and Adaptive Thresholding Based Segmentation Method for Breast Cancer Diagnosis , 2011, Computer Recognition Systems 4.