News | Lung Cancer | May 09, 2017

Third Annual Data Science Bowl Winners Advance Low-Dose CT Lung Cancer Screening

Booz Allen Hamilton and Kaggle competition nets nearly 18,000 algorithms aimed at unlocking the lifesaving potential of cancer screening

Third Annual Data Science Bowl Winners Advance Low-Dose CT Lung Cancer Screening

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

Related Content

SimonMed Deploys ClearRead CT Enterprise Wide
News | Computer-Aided Detection Software | September 17, 2018
September 17, 2018 — National outpatient physician radiology group SimonMed Imaging has selected Riverain Technologie
Siemens Healthineers Announces First U.S. Install of Somatom go.Top CT
News | Computed Tomography (CT) | September 17, 2018
September 17, 2018 — The Ohio State University Wexner Medical Center in Columbus recently became the first healthcare
Veye Chest version 2
News | Lung Cancer | September 11, 2018
Aidence, an Amsterdam-based medical AI company, announced that Veye Chest version 2, a class IIa medical device, has
The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

Sponsored Content | Case Study | Radiation Dose Management | September 07, 2018
Radiation dose management is central to child patient safety. Medical imaging plays an increasing role in the accurate...
Carestream Releases Second-Generation Metal Artifact Reduction Software for OnSight 3D Extremity System
Technology | Computed Tomography (CT) | September 06, 2018
Carestream Health has started shipping a new software version for its Carestream OnSight 3D Extremity System that...

Image courtesy of Siemens Healthineers

Feature | CT Angiography (CTA) | September 06, 2018 | Dave Fornell
There have been a few big, recent advancements in cardiac computed tomography angiography (CCTA) imaging technology....
Key Patient Preparations for a CT Scan
News | Computed Tomography (CT) | September 05, 2018
The Center for Diagnostic Imaging (CDI) in Miami recently released a list of important preparations patients should...
iSchemaView RAPID Technology Now Installed in More Than 500 Stroke Centers
News | Neuro Imaging | August 27, 2018
iSchemaView announced that more than 575 stroke centers in 22 countries have selected the RAPID advanced imaging...
RSNA Announces Pneumonia Detection Machine Learning Challenge
News | Artificial Intelligence | August 27, 2018
The Radiological Society of North America (RSNA) has launched its second annual machine learning challenge. The RSNA...
Doctor-Patient Discussions Neglect Potential Harms of Lung Cancer Screening
News | Lung Cancer | August 15, 2018
August 15, 2018 — Although national guidelines advise doctors to discuss the benefits and harms of...