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.

UChicago awarded $20 million to host COVID-19 medical imaging center

Via University of Chicago | August 7, 2020

Two-year federal contract will support open-source database, enable AI-driven research

A new center hosted at the University of Chicago—co-led by the largest medical imaging professional organizations in the country—will help tackle the ongoing COVID-19 pandemic by curating a massive database of medical images to help better understand and treat the disease.

Led by Prof. Maryellen Giger of UChicago Medicine, the Medical Imaging and Data Resource Center (MIDRC) will create an open-source database with medical images from thousands of COVID-19 patients. The center will be funded by a two-year, $20 million contract from the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health (NIH)… Continue reading.

Researchers to develop AI to help diagnose, understand COVID-19 in lung images

Via University of Chicago | May 6, 2020

UChicago, Argonne study hopes to learn to identify cases and guide treatment

As physicians and researchers grapple with a rapidly-spreading, deadly and novel disease, they need all the help they can get. Many centers are exploring whether artificial intelligence can help fight COVID-19, extracting knowledge from complex and rapidly growing data on how to best diagnose and treat patients.

One University of Chicago and Argonne National Laboratory collaboration believes that AI can be a helpful clinical partner for a particularly important kind of medical data: images. Because severe cases of COVID-19 most often present as a respiratory illness, triggering severe pneumonia in patients, chest X-rays and thoracic CT scans are a potential exam. With a grant from the new Digital Transformation Institute, computer-aided diagnosis expert Maryellen Giger will lead an effort to develop new AI tools that use these medical images to diagnose, monitor and help plan treatment for COVID-19 patients… Continue reading.

AI For Breast Ultrasound and MRI

Via RSNA | April 13, 2020

Maryellen L. Giger, PhD discusses AI For Breast Ultrasound and MRI.

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.

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.

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.