Data scientists compete to create cancer-detection algorithms
Data scientists are using machine learning to tackle lung cancer detection. Beginning in January, nearly 10,000 data scientists around the world competed in the Data Science Bowl to develop the most effective algorithm to help medical professionals detect lung cancer earlier and with better accuracy.
In 2010, the National Lung Screening Trial showed that annual screening with low-dose computed tomography (CT) — a scanner that uses computer-processed combinations of many X-ray images from different angles to generate high-contrast 3D images — could reduce lung cancer deaths by 20 percent. While a breakthrough for early detection, the technology has also resulted in a relatively high rate of false positives compared with more traditional X-rays.
“It’s a really powerful approach that’s reduced cancer deaths by 20 percent, but there’s a very high rate of false positives,” says Anthony Goldbloom, CEO of machine learning company Kaggle, which, with partner Booz Allen Hamilton, presents the annual Data Science Bowl. “A huge amount of people have been told they have cancer only find out later that they don’t. There’s a human cost to that. It’s incredibly stressful.”
So for this year’s Data Science Bowl, Booz Allen and Kaggle decided to direct the power of data science and machine learning to tackle the false positives problem. The partners secured a $1 million prize purse, funded by the Laura and John Arnold Foundation, to be split among the top-10 contestants.
Booz Allen and Kaggle created the Data Science Bowl in 2015 in an effort to focus data scientists on social good, says Josh Sullivan, senior vice president and chief data scientist for Booz Allen.
“We wanted to create something that galvanized people to come together to do something for social good, something bigger than themselves,” he says. “How can we do something for social good that’s pretty substantial? We wanted it to be something that would result in scientific discovery. Something open to the public; not for our benefit or our clients’ benefit, but open source and crowd sourced to people around the world.”
Sullivan says more than 300 ideas were submitted for the focus of the third annual Data Science Bowl (previous Data Science Bowls have focused on algorithms for determining ocean health and detecting heart disease). Ultimately, he says, the partners decided they would help the National Cancer Institute (NCI) with its Beau Biden Cancer Moonshot, an effort to accelerate cancer research to make more therapies available to more patients, and to improve cancer prevention and early detection.
NCI supplied the Data Science Bowl with 2,000 anonymized, high-resolution CT scans, each image containing gigabytes of data. Sullivan says 1,500 of the images were the training set, accompanied by the final diagnosis. The remaining 500 images were the problem set.
