Classification of breast cancer histology images using Convolutional Neural Networks

PLoS One. 2017 Jun 1;12(6):e0177544. doi: 10.1371/journal.pone.0177544. eCollection 2017.

Abstract

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%.

MeSH terms

  • Breast Neoplasms / classification
  • Breast Neoplasms / pathology*
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Neural Networks, Computer*
  • Support Vector Machine

Grant support

Project "NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016" is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Teresa Araújo is funded by the grant contract SFRH/BD/122365/2016 (Fundação para a Ciência e a Tecnologia). Guilherme Aresta is funded by the grant contract SFRH/BD/120435/2016 (Fundação para a Ciência e a Tecnologia). José Rouco is funded by the grant contract SFRH/BPD/79154/2011 (Fundação para a Ciência e a Tecnologia).