May 9, 2017 — Management consulting firm Booz Allen Hamilton and data science company Kaggle recently 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.
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.
Using anonymized high-resolution lung scans in one of the largest datasets to be made publicly available, provided by the National Cancer Institute (NCI), participants created algorithms that can improve lung cancer screening technology. The participants created algorithms that can accurately determine when lesions in the lungs are cancerous and dramatically decrease the false positive rate of current low-dose CT technology.
Top teams will present their winning solutions at the 2017 GPU Technology Conference, May 8-11 in San Jose, Calif., hosted by NVIDIA, a Data Science Bowl sponsor.
“Reducing the false-positive rate of low-dose CT scans is a critical step in improving the accuracy of CT screening of lung cancer and having a positive impact on public health,” said Keyvan Farahani, program director, National Cancer Institute, who provided scientific guidance regarding the competition’s design and datasets. “NCI is committed to working closely with the scientific community, the [U.S.] Food and Drug Administration, and other stakeholders to utilize this year’s top-ranking solutions to further advance the field of lung cancer screening.”
For more information: www.datasciencebowl.com