Crowd-sourcing inspiration: Data science contest asks entrants to diagnose lung cancer

William Doss | | Radiology Business | Imaging Informatics

A $1 million data science contest organized by Booz Allen Hamilton asked entrants to design deep-learning algorithms to identify lung cancer using just 2,000 images—a small data set in the machine learning world. While the winning entry won’t necessarily be used in clinical settings, the contest highlights the potential for crowd-sourcing inspiration.  

The winning team taught a neural network to first identify nodules in low-dose CT images from the National Cancer Institute before diagnosing cancer.

“We think that explicitly dividing this problem into two stages is critical, which seems also to be what human experts would do,” said Zhe Li, a member of the winning team and a student at China’s Tsinghua University, in an interview with MIT Technology Review.

The winning algorithms are available for free online, allowing medical researchers and data scientists to draw from minds outside the industry—what better way to think outside of the (black) box?

Read the full story here:

Million-Dollar Prize Hints at How Machine Learning May Someday Spot Cancer

William Doss

As a Senior Writer for TriMed Media Group, Will covers radiology practice improvement, policy, and finance. He lives in Chicago and holds a bachelor’s degree in Life Science Communication and Global Health from the University of Wisconsin-Madison. He previously worked as a media specialist for the UW School of Medicine and Public Health. Outside of work you might see him at one of the many live music venues in Chicago or walking his dog Holly around Lakeview.

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