Automated Gleason Grading of Prostate Biopsies using Deep Learning

@article{Bulten2019AutomatedGG,
  title={Automated Gleason Grading of Prostate Biopsies using Deep Learning},
  author={Wouter Bulten and Hans Pinckaers and Hester van Boven and Robert Vink and Thomas de Bel and Bram van Ginneken and Jeroen van der Laak and C. A. Hulsbergen-van de Kaa and Geert J. S. Litjens},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.07980}
}
  • Wouter Bulten, Hans Pinckaers, +6 authors Geert J. S. Litjens
  • Published in ArXiv 2019
  • Medicine, Engineering, Computer Science
  • The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was developed using 5834 biopsies from 1243 patients. A semi-automatic labeling technique was used to circumvent the need for full manual annotation by pathologists. The developed system achieved a high agreement with the reference standard. In a separate observer… CONTINUE READING

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