Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and…

• UD
  U.S. Department of Defense
• RC
  Rutgers Cancer Institute of New Jersey
• DA
  Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
• DO
  Department of Science and Technology, Ministry of Science and Technology, India
• UN
  Universidad Nacional de Colombia
• NI
  National Institutes of Health
• NC
  National Cancer Institute
• NI
  National Institute of Diabetes and Digestive and Kidney Diseases