Latest news3:
The MIDOG 2022 challenge report was published in Medical Image Analysis (Elsevier).
Please find the paper here: link.
Latest news2:
Unfortunately, we've had an error in the previous calculation of the additional Average Precision (AP) metric. Please find out all details in this blog post.
Latest news:
We are excited to announce that the MIDOG++ dataset, an extension of the MIDOG2022 training data set has just been released.
We have published a data descriptor paper for a full description of the data set.
To get a glimpse, we recommend reading the blog post about it
This is the MIDOG 2022 microscopy domain generalization challenge!
Website: https://midog.deepmicroscopy.org
Motivation:
- Mitosis detection is a key component of tumor prognostication for various tumors.
- Modern deep learning architectures provide detection accuracies for mitosis that are on the level of human experts.
- Mitosis is known to be relevant for many tumor types, yet, when trained on one tumor / tissue type, the performance will typically drop significantly on another.
Scope:
- Detect mitotic figures (cells undergoing cell division) from histopathology images (object detection).
- You will be provided with images from 6 different tumor types, 5 out of which are labeled. In total the set consists of 405 cases and includes 9501 mitotic figure annotations in the training set.
- Evaluation will be done on ten different tumor types with the F1 score as main metric.
How to participate:
- Register on the challenge website and download the data set.
- Select a track (see below) that you want to participate in.
- 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. A template can be found under this link.
Note: Only your own work will qualify as a submission.
Tracks:
- There's different ways to approach this task, thus we split up the challenge into two tracks that participants can participate in (either in one or in both).
- Track 1 prohibits the use of any additional (i.e. not provided by the challenge) data and also any kind of manual or semi-automatic generation of any kind of label information.
- In Task 2, use of any publicly available data sets and additional labels is permitted. If you want to make use of previously private labels, please have a look at the related blog post.
Important Dates:
- April 20: Training set released
- August 5th: Preliminary test set available
August 15thAugust 23th: Deadline for registration of participantsAugust 17thAugust 25th: Deadline for trial runs on preliminary test set (extended)August 29thAugust 30th: Docker container submission (with link to published preprint) (extended)- Sept 18-22: Announcement of results at MICCAI 2022
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). We are currently reaching out to sponsors for prize money.
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
- Samir Jabari, Institute of Neuropathology, University Hospital Erlangen, Germany
- Katharina Breininger, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany