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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.

Artificial intelligence software for breast cancer diagnosis makes TIME’s list of Best Inventions for 2019

Via U Chicago Medicine | December 4, 2019

Artificial intelligence software developed by University of Chicago Medicine researchers to help radiologists more accurately diagnose breast cancer made TIME’s list of Inventions for 2019.

QuantX — the first-ever, FDA-cleared software to aid in breast cancer diagnosis — aims to reduce missed cancers as well as false positives that can lead to unnecessary biopsies. The technology is based on two decades of research by Maryellen Giger, PhD, Professor of Radiology and a world-renowned pioneer in computer-aided diagnosis (CAD)… Continue reading.

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Doctors find new ways to reduce unnecessary breast biopsies

Via KTVA | August 6, 2019

One out of eight women will be diagnosed with breast cancer at some point in her lifetime. Early detection is the best tool to increase survival rates. Now researchers are looking at a new way to confirm cancer faster during a mammogram while reducing the need for additional testing.

It’s a terrifying moment for any woman. One doctor says they have found something during her mammogram.

“Many women are recalled unnecessarily, which causes anxiety. Do I have cancer or not?” said Karen Drukker, Ph.D., Research Associate Professor at the University of Chicago… Continue reading.

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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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