Deep learning powers cancer diagnosis in digital pathology

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations


Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporating deep learning-based approaches to mine the subvisual morphometric phenotypes for improving pathology diagnosis. In this perspective paper, we provide an overview on major deep learning approaches for digital pathology and discuss challenges and opportunities of such approaches to aid cancer diagnosis in digital pathology. In particular, the emerging graph neural network may further improve the performance and interpretability of deep learning in digital pathology.

Original languageEnglish (US)
Article number101820
JournalComputerized Medical Imaging and Graphics
StatePublished - Mar 2021


  • AI
  • Digital pathology
  • cancer diagnosis
  • deep learning
  • graph neural networks
  • microscopy image

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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