Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective…

• DA
  Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)

  Awards: 528/2011, 0213-2013
• NI
  National Institutes of Health

  Awards: R01CA202752, 1U24CA199374-01, R01CA202752-01A1, R21CA195152-01, R21CA179327-01, R01CA208236-01A1, 1U24CA199374
• NC
  National Cancer Institute

  Awards: R21CA179327-01, R21CA179327, 1U24CA199374-01, R01CA202752-01A1 R01CA208236-01A1 R21CA179327-01; R21CA195152-01, R21CA195152-01, R01CA208236, R01CA202752, 1U24CA199374, R21CA195152, 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1
• NI
  National Institute of Diabetes and Digestive and Kidney Diseases