Classification of breast cancer histology images using Convolutional Neural Networks. 2017

Teresa Araújo, and Guilherme Aresta, and Eduardo Castro, and José Rouco, and Paulo Aguiar, and Catarina Eloy, and António Polónia, and Aurélio Campilho
Faculdade de Engenharia da Universidade do Porto (FEUP), R. Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.

Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

UI MeSH Term Description Entries
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D001943 Breast Neoplasms Tumors or cancer of the human BREAST. Breast Cancer,Breast Tumors,Cancer of Breast,Breast Carcinoma,Cancer of the Breast,Human Mammary Carcinoma,Malignant Neoplasm of Breast,Malignant Tumor of Breast,Mammary Cancer,Mammary Carcinoma, Human,Mammary Neoplasm, Human,Mammary Neoplasms, Human,Neoplasms, Breast,Tumors, Breast,Breast Carcinomas,Breast Malignant Neoplasm,Breast Malignant Neoplasms,Breast Malignant Tumor,Breast Malignant Tumors,Breast Neoplasm,Breast Tumor,Cancer, Breast,Cancer, Mammary,Cancers, Mammary,Carcinoma, Breast,Carcinoma, Human Mammary,Carcinomas, Breast,Carcinomas, Human Mammary,Human Mammary Carcinomas,Human Mammary Neoplasm,Human Mammary Neoplasms,Mammary Cancers,Mammary Carcinomas, Human,Neoplasm, Breast,Neoplasm, Human Mammary,Neoplasms, Human Mammary,Tumor, Breast
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D060388 Support Vector Machine SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples. Support Vector Network,Machine, Support Vector,Machines, Support Vector,Network, Support Vector,Networks, Support Vector,Support Vector Machines,Support Vector Networks,Vector Machine, Support,Vector Machines, Support,Vector Network, Support,Vector Networks, Support

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