AI Cervical Spine Fracture Detection Challenge with Prize Pool of $30,000

Kaggle is offering RSNA 2022 Cervical Spine Fracture Detection Challenge. RSNA has teamed with the American Society of Neuroradiology (ASNR) and the American Society of Spine Radiology (ASSR) to conduct an AI challenge competition exploring whether artificial intelligence can be used to aid in the detection and localization of cervical spine fractures.

To create the ground truth dataset, the challenge planning task force collected imaging data sourced from twelve sites on six continents, including approximately 3,000 CT studies. Spine radiology specialists from the ASNR and ASSR provided expert image level annotations these studies to indicate the presence, vertebral level and location of any cervical spine fractures.

In this challenge competition, you will try to develop machine learning models that match the radiologists' performance in detecting and localizing fractures to the seven vertebrae that comprise the cervical spine. Winners will be recognized at an event during the RSNA 2022 annual meeting.

TOTAL PRIZES AVAILABLE: $30,000:

• 1st Place - $6,000
• 2nd Place - $5,000
• 3rd Place - $4,000
• 4th - 8th Places - $3,000 each

• Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Sponsor reserves the right to award alternative Prizes where needed to comply with local laws.
• Submissions to this competition must be made through Notebooks. In order for the "Submit" button to be active after a commit, the following conditions must be met:

1. CPU Notebook <= 9 hours run-time
2. GPU Notebook <= 9 hours run-time
3. Internet access disabled
4. Freely & publicly available external data is allowed, including pre-trained models
5. Submission file must be named submission.csv
6. Please see the Code Competition FAQ for more information on how to submit. And review the code debugging doc if you are encountering submission errors.

• One account per participant: You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
• No private sharing outside teams: Privately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.
• Team Mergers: Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
• Team Limits: The maximum team size is 5.
• Submission Limits: You may submit a maximum of 5 entries per day. You may select up to 2 final submissions for judging.
• Check more competition-specific terms here.

Evaluation:

Submissions are evaluated using a weighted multi-label logarithmic loss. Each fracture sub-type is its own row for every exam, and you are expected to predict a probability for a fracture at each of the seven cervical vertebrae designated as C1, C2, C3, C4, C5, C6 and C7. There is also an any label, patient_overall, which indicates that a fracture of ANY kind described before exists in the examination. Fractures in the skull base, thoracic spine, ribs, and clavicles are ignored. The any label is weighted more highly than specific fracture level sub-types.

About Kaggle:

Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

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