Booz Allen Hamilton and Kaggle competition nets nearly 18,000 algorithms aimed at unlocking the lifesaving potential of cancer screening
- The National Cancer Institute will work on winning solutions closely with the scientific community and other stakeholders to advance low dose CT lung cancer screening
- Winners will split a prize purse of $1 million, the largest-ever for the competition, funded by the Laura and John Arnold Foundation
May 2, 2017
McLean, VA — Booz Allen Hamilton (NYSE: BAH) and Kaggle today announced the winners of the third annual Data Science Bowl, a competition that harnesses the power of data science and crowdsourcing to tackle some of the world’s toughest problems. This year’s challenge brought together nearly 10,000 participants from across the world. Collectively they spent more than an estimated 150,000 hours and submitted nearly 18,000 algorithms—all aiming to help medical professionals detect lung cancer earlier and with better accuracy.
2017 Data Science Bowl winners include:
- First Place: Liao Fangzhou and Zhe Li, two researchers from China’s Tsinghua University who have no formal medical background but were able to apply their analytics skills to an unfamiliar but challenging area of research.
- Second Place: Julian de Wit and Daniel Hammack, both software and machine learning engineers based in the Netherlands. Julian came in third in the Data Science Bowl 2016.
- Third Place: Team Aidence, members of which work for a Netherlands-based company that applies deep learning to medical image interpretation.
Josh Sullivan, senior vice president at Booz Allen, said, “The Data Science Bowl shows that the power of collective ingenuity, data science and advanced analytics can be harnessed to tackle society’s toughest challenges like eradicating cancer. This year’s complex problem—improving the accuracy of lung cancer screening—required the diversity of perspectives and approaches that only a crowd-sourced challenge like the Data Science Bowl can provide. We look forward to advancing these solutions and in the fight against cancer.”
Lung cancer is the most common type of cancer worldwide, affecting nearly 225,000 people each year in the United States alone. Low-dose computed tomography (CT) is a breakthrough technology for early detection, with the potential to reduce lung cancer deaths by 20 percent. But, the technology must overcome a relatively high false positive rate.