Artificially intelligent pathology Public Deposited

Controlling cancer requires comprehensive understanding of the molecular, cellular, and organizational properties of tumor tissue. While clinical pathology has served as a gold standard for cancer diagnosis for over a century, the field continues to largely rely on visual inspection of sectioned and stained tissue under the microscope by expert pathologists. Emerging technological advancements in scanning equipment have contributed to wide-scale digitization of whole slide images with clear clinical connotation, while parallel strides in artificial intelligence have enabled computational models of computer vision shown to meet or exceed human capabilities for identifying features of interest and abnormalities in imaging data. This work integrates deep learning systems for histopathological image analysis to quantitatively and qualitatively evaluate spatial characteristics of tumor biology to better guide clinical diagnosis and treatment of cancer.

  • Schau.Geoffrey.2020.pdf
Publication Date
  • 2020
Document type