Clog Loss: Advance Alzheimer’s Research with Stall Catchers Hosted By MathWorks
Help accelerate Alzheimer's research by automatically classifying which blood vessels are flowing and which are stalled.
We see tremendous opportunity to incorporate these models into Stall Catchers, where they could work in concert with human-based analysis to double or triple our analytic throughput without compromising data quality. This could get us closer to an Alzheimer’s treatment at unprecedented analytic speeds.
— Pietro Michelucci, Project Lead, Stall Catchers
5.8 million Americans live with Alzheimer’s dementia, including 10% of all seniors 65 and older. Scientists at Cornell have discovered links between “stalls,” or clogged blood vessels in the brain, and Alzheimer’s. The ability to prevent or remove stalls may transform how Alzheimer’s disease is treated. However, finding these stalls is extremely time intensive, especially as only around 1% of image stacks contain stalls.
In this challenge, participants were tasked with building machine learning models that could classify blood vessels in 3D image stacks as stalled or flowing. The exceptional challenge dataset came from Stall Catchers, a citizen science project that crowdsources the identification of stalls using research data provided by Cornell University’s Department of Biomedical Engineering.
The researchers behind Stall Catchers believe that some portion of the data may be within reach of machine learning models. Model predictions would only be used in cases where models had been validated to meet the researchers’ data quality requirements.
The top-performing model was able to achieve an impressive 0.855 Matthew's correlation coefficient (MCC) score, surpassing 85% sensitivity (true positive rate, i.e. classifying stalled vessels as such) and 99% specificity (true negative rate, i.e. classifying flowing vessels as such). This level of specificity means that models like this can be used to process large volumes of data while minimizing the risk of losing the stalled cases that are so important to research.
The greatest immediate promise from these results lies in combining machine and human predictions to accelerate research, by 1) using algorithms and humans in tandem to process data far more quickly, and 2) continuing experimentation to develop methods that combine algorithms and humans in ways that exceed the performance that either demonstrates alone. All prize-winning solutions from this competition are linked below and made available for anyone to use and learn from.
RESULTS ANNOUNCEMENT + MEET THE WINNERS
Clog Loss: Advance Alzheimer’s Research with Stall Catchers: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
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Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Public dissemination of entries
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Qualified open licensing of winners
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT), with the exception of uses of Sponsor-provided software as described on the Competition Website, in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the provided Winning Model Documentation Template in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
External data is not allowed unless otherwise noted explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
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Selection of winners
This Competition is a challenge of skill and the final results are determined by evaluating a combination of quantitative and qualitative factors, as more fully described on the Competition Website. For that portion of the Competition evaluated quantitatively, the results will be determined solely by leaderboard ranking on the private leaderboard based on an objective statistical evaluation metric published on the Competition Website prior to commencement of the Competition (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the objective statistical evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data. For that portion of the Competition evaluated qualitatively, the results will be determined by a panel of judges, as more fully described on the Competition Website. As more fully described on the Competition Website, the Competition may have a quantitative winner, a qualitative winner, a combined quantitative and qualitative winner, or all three.
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Data use and code sharing
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