MIDOG Challenge Series
MIDOG 2021 MItosis DOmain Generalization Challenge · MICCAI 2021

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MIDOG 2021 on grand-challenge.org

MIDOG 2021 is over. If you are interested in joining it's successor, MIDOG 2022, here is a link: https://midog2022.grand-challenge.org

If you are interested in the challenge paper, please find it at: https://www.sciencedirect.com/science/article/abs/pii/S1361841522003279

We invite you to participate in the MIDOG 2021 microscopy domain generalization challenge!

Website

Motivation

  • Mitosis detection is a key component of tumor prognostication for various tumors, including breast cancer.
  • Scanning microscopy slides with different scanners leads to a significant visual difference, resulting in a domain shift.
  • This domain shift prevents most deep learning models from generalizing to other scanners, leading to strongly reduced performance.

Scope

  1. Detect mitotic figures (cells undergoing cell division) from histopathology images (object detection).
  2. You will be provided with images scanned by 4 different scanners, 3 out of which are labeled. In total the set consists of 200 cases of breast cancer.
  3. Evaluation will be done on four scanners (two new, two are part of the training set) with the F1 score as main metric.

How to participate:

  • Register on the challenge website and download the data set.
  • Submit docker container(s) with your algorithm(s) (template provided here: https://github.com/DeepPathology/MIDOG_reference_docker).
  • Provide a short paper (preprint) about your method.
  • Note: Only your own work will qualify as a submission.

Important Dates:

  • August 15th: Preliminary test set available
  • August 21th: Deadline for registration of participants
  • Sept 2nd: Deadline for docker container submission and preprint abstract submission (extended)
  • Oct 1st: Announcement of results at MICCAI 2021

Publications and Prizes:

  • All participants of the MIDOG challenge may submit a short LNCS Springer paper (up to 4 pages) with a deadline of three weeks after the workshop, which will be subject to peer review.
  • Top performing teams will be invited to contribute to a challenge overview paper, which will be submitted to a high impact journal (MedIA/TMI).
  • Best contribution (as of F1 score on test set) will receive 500€, second 300€ and third 200€.

Organizers:

  • Marc Aubreville, Technische Hochschule Ingolstadt, Germany
  • Christof Bertram, Institute of Pathology, University of Veterinary Medicine, Vienna, Austria
  • Mitko Veta, Medical Image Analysis Group, TU Eindhoven, The Netherlands
  • Nikolas Stathonikos, Pathology Department, UMC Utrecht, The Netherlands
  • Robert Klopfleisch, Institute of Veterinary Pathology, Freie Universität Berlin, Germany
  • Katharina Breininger, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander- Universität Erlangen-Nürnberg, Germany
  • Natalie ter Hoeve, Pathology Department, UMC Utrecht, The Netherlands
  • Francesco Ciompi, Computational Pathology Group, Radboud UMC Nijmegen, The Netherlands
  • Andreas Maier, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany