Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images Academic Article uri icon


MeSH Major

  • Enzyme Inhibitors
  • Nitric Oxide Synthase Type II
  • Ribosomal Proteins
  • Triple Negative Breast Neoplasms
  • omega-N-Methylarginine


  • Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at

publication date

  • January 2017



  • Academic Article



  • eng

PubMed Central ID

  • PMC5828543

Digital Object Identifier (DOI)

  • 10.1016/j.ebiom.2017.12.026

PubMed ID

  • 29292031

Additional Document Info

start page

  • 317

end page

  • 328