DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Mobile Imaging
Current Location:
> This Story

Log in or Register to rate this News Story
Forward Printable StoryPrint Comment



X-Ray Homepage

First radiology center opens in Zimbabwe capital to help curb shortage Currently 115 radiologists in country of 14 million people

Guerbet and IBM Watson to develop AI-based liver diagnostics solution Supports liver diagnostics in CT and MR

MITA calls for timely exemption process for Section 301 tariffs Calls for exemption of medical imaging technology

KT Corporation and Russian Railways launch Russian digital health system Assist providers in all 173 Russian Railway stations

Siemens announces first global installation of Biograph Vision PET/CT System yields better temporal resolution and sensitivity than previously reported

Eye movement could cause errors in mammogram interpretation, study finds Movement patterns during mammograms signify bias that affects interpretations

Andrew J. Evans and Erik P. Sulman NYU Langone Health hires nationally renowned radiation oncologists

Photoacoustic CT tech developed for early stage breast cancer detection Safer, more efficient, and more affordable than mammography, MR and ultrasound: developer

MIT research yields more efficient anatomical 3D printing How 'dithered bitmaps' may increase accessibility of 3D printing in imaging

In PET and SPECT, getting more sensitive and working smarter A look at the newest systems on the market

A new AI approach creates synthetic
X-rays that teach AI systems to identify
rare conditions in authentic imagery

Not enough imaging data to train AI on rare pathologies? Create it!

by John R. Fischer , Staff Reporter
Canadian researchers have developed a new AI-based approach for identifying rare pathologies despite a scarcity in the number of X-ray images available for such conditions.

Utilizing machine learning, members of the University of Toronto and St. Michael’s Hospital created computer generated X-rays for the purpose of training AI systems to identify infrequent conditions in authentic X-ray images.

Story Continues Below Advertisement

RaySafe helps you avoid unnecessary radiation

RaySafe solutions are designed to minimize the need for user interaction, bringing unprecedented simplicity & usability to the X-ray room. We're committed to establishing a radiation safety culture wherever technicians & medical staff encounter radiation.

“The X-ray database at St. Mike's was unbalanced. There were more cases of cardiomegaly as compared to pneumothorax. This is consistent with the number of cases in practice,” Professor Shahrokh Valaee, a professor in The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) at the University of Toronto, told HCB News. “Unfortunately, it is easier for a radiologist to miss those rare cases. By synthesizing X-rays we were able to increase the size of the database and to balance it. We show that the accuracy of detecting those rare cases increases dramatically.”

The potential for AI relies on the availability of data in the form of thousands of labeled images. Such amounts though do not exist for certain conditions, preventing speedy and accurate medical diagnostics through machine learning.

The team generated and continually improved upon simulated images utilizing an approach known as deep convolutional generative adversarial network (DCGAN), which is actually composed of two networks. One enables the generation of images while the other tries to discriminate between synthetic and authentic ones. The two are trained to the point where the discriminator is unable to differentiate real from synthesized ones.

Upon obtaining the necessary number of artificial X-rays needed, researchers then combined them with real X-rays to train a deep convolutional neural network to identify normal images and ones depicting a variety of conditions.

Accuracy between the augmented dataset and the original were compared by the Machine Intelligence in Medicine Lab (MIMLab), a group consisting of physicians, scientists and engineering researchers working together in image processing, AI and medicine to solve medical challenges.

Feeding both sets of data through their AI system, the MIMLab found classification accuracy improved by 20 percent for common conditions and 40 percent for rare ones.

Seeing these improvements, the team is confident that artificial X-rays can accurately be used for diagnoses, allowing researchers outside the hospital premises to access them immediately for fast use without violating patient privacy concerns.

GAN, according to Valaee, has been utilized in other areas of healthcare and has just recently been introduced to medical imaging for assessing eye and liver conditions.

“GAN-generated images can be used in retina and liver diseases,” said Valaee. “I expect that many more cases can also benefit from synthesized images.”

X-Ray Homepage

You Must Be Logged In To Post A Comment

Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Gold Service Dealer Program
Receive RFP/PS
Healthcare Providers
See all
HCP Tools
A Job
Parts Hunter +EasyPay
Get Parts
Recently Certified
View Recently
Certified Users
Recently Rated
View Recently
Certified Users
Rental Central
Rent Equipment
For Less
Sell Equipment/Parts
Get The
Most Money
Service Technicians Forum
Find Help
And Advice
Simple RFP
Get Equipment
Virtual Trade Show
Find Service
For Equipment
Access and use of this site is subject to the terms and conditions of our LEGAL NOTICE & PRIVACY NOTICE
Property of and Proprietary to, Inc. Copyright ©2001-2018, Inc.