Gleason Grading of Prostate Biopsies

Logo for Gleason Grading of Prostate Biopsies


Last updated:
Nov. 19, 2020, 6:33 p.m.
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Automated Gleason grading of prostate biopsies following the Gleason Grade Group system.

Contact Information

Questions related to this algorithm can be addressed to Wouter Bulten or Geert Litjens.

Additional Information

Please take the following limitations in to account when submitting data to this algorithm: A custom normalization algorithm is trained on the input data, though this cannot overcome all stain and scanner differences. We limit the overall processing time of each job by setting an upper bound on the number of epochs the normalization algorithm is trained. Due to the limited processing time, in some cases the normalization technique can fail. All input images should contain magnification levels that correspond to a 0.5, 1.0, and 2.0μm pixel spacing (± 0.05). The algorithm will stop if any of the input images miss one or more of these levels.

NOTE: Submitted data can be used for future research projects. We assume that submitted data has been anonymized and that you, as a submitter, have the right to submit the image.


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 system using deep learning that can grade prostate biopsies following the Gleason Grading System. A description of our method and research can be found in the paper: "Automated deep learning system for Gleason grading of prostate biopsies: a development and validation study". A summary is available in a blog post on our study.

Uses and Directions

This algorithm was developed for research purposes only.