Gleason Grading of Prostate Biopsies (non-normalized)


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About

Image Version:
b4884ac0-d460-41c6-8d7f-aaa433080d83
Last updated:
Nov. 19, 2020, 4:15 p.m.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
  • Generic Medical Image (Image)
  • Generic Overlay (Heat Map)
  • Results JSON File (Anything)
  • Model Facts

    Summary

    Automated Gleason grading of prostate biopsies following the Gleason Grade Group system. This version of the algorithm runs without data normalization.

    Contact Information

    Questions related to this algorithm can be addressed to:

    https://www.computationalpathologygroup.eu/members/wouter-bulten/

    or

    https://www.computationalpathologygroup.eu/members/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.

    Mechanism

    Validation and Performance

    Uses and Directions

    This algorithm was developed for research purposes only.

    Warnings

    Common Error Messages

    Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3