Call for Participation - MICCAI 2021 Challenge: MOOD: Medical Out-of-Distribution Analysis Challenge

[David Z]

MICCAI 2021 Challenge:

MOOD: Medical Out-of-Distribution Analysis Challenge

http://medicalood.dkfz.de/web/

"Will your algorithm be able to spot a gorilla in a CT scan ?"

Despite overwhelming successes in recent years, progress in the field of biomedical image computing still largely depends on the availability of annotated training examples. This annotation process is often prohibitively expensive because it requires the valuable time of domain experts. Furthermore, many algorithms used in image analysis are vulnerable to out-of-distribution samples, resulting in wrong and overconfident decisions.

However, even humans are vulnerable to ‘inattentional blindness’. More than 50% of trained radiologists did not notice a gorilla image, rendered into a lung CT scan when assessing lung nodules.

Challenge participation

You are invited to join this challenge, where we approach the task of automated unsupervised anomaly/ out-of-distribution detection. The MOOD Challenge gives a standardized dataset and benchmark for anomaly detection. We propose two different tasks. First a sample-level (i.e. patients-level) analysis, thus detecting out-of-distribution samples. For example, having a pathological condition or any other condition not seen in the training-set. This can pose a problem to classically supervised algorithms and can further allow physicians to prioritize different patients. Secondly, we propose a voxel-level analysis i.e. giving a score for each voxel, highlighting abnormal conditions and potentially guiding the physician.

Want more information or take part? visit our Website, Github, or Submission-Site.

Important dates 

• Challenge Data Release: 01 May 2021

• Registration Until: 06 September 2021

• Submission of Dockers closes: 07 September 2021

• Submit abstract before: 21 September 2021

• Announcement of Results: 3rd October @ Miccai 2021

The top participating teams will be invited to present their approach at the MICCAI event and publish a paper about their approach as MICCAI proceedings.

Looking forward to receiving your submissions. 

 

The MOOD Organization Team