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Maryellen Giger, Ph.D.

AIMBE College of Fellows Class of 2000
For the development of significant and fundamental algorithms and software for detecting breast cancer from mammographic images.

How machine learning can succeed in medical imaging

Via Health Imaging | March 1, 2018

In an editorial in the March issue of the Journal of the American College of Radiology, Maryellen L. Giger, PhD, and professor of radiology at the University of Chicago, discussed what must occur for machine learning to succeed in health imaging and what clinicians and patients should expect in the future from the synergy of medical imaging and artificial intelligence.

Risk assessment, detection, diagnosis and therapy response are a few examples of radiological imaging tasks that have advanced and benefited from the implementation of machine learning technology.

“For deep learning in radiology [and health imaging] to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly and subtle differences in disease states are more difficult to perceive than differences in everyday objects,” Giger wrote… Continue reading.

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