Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Training artificial intelligence with artificial X-rays

09.07.2018

New research could help AI identify rare conditions in medical images by augmenting existing datasets

Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to 'teach' the algorithms what to look for.


On the left of each quadrant is a real X-ray image of a patient's chest and beside it, the syntheisized X-ray formulated by the DCGAN. Under the X-ray images are corresponding heatmaps, which is how the machine learning system sees the images.

Credit: Hojjat Salehinejad/MIMLab

Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting.

Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets.

"In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) at the University of Toronto. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays."

Valaee is a member of the Machine Intelligence in Medicine Lab (MIMLab), a group of physicians, scientists and engineering researchers who are combining their expertise in image processing, artificial intelligence and medicine to solve medical challenges. "AI has the potential to help in a myriad of ways in the field of medicine," says Valaee. "But to do this we need a lot of data -- the thousands of labelled images we need to make these systems work just don't exist for some rare conditions."

To create these artificial X-rays, the team uses an AI technique called a deep convolutional generative adversarial network (DCGAN) to generate and continually improve the simulated images. GANs are a type of algorithm made up of two networks: one that generates the images and the other that tries to discriminate synthetic images from real images. The two networks are trained to the point that the discriminator cannot differentiate real images from synthesized ones. Once a sufficient number of artificial X-rays are created, they are combined with real X-rays to train a deep convolutional neural network, which then classifies the images as either normal or identifies a number of conditions.

"We've been able to show that artificial data generated by a deep convolutional GANs can be used to augment real datasets," says Valaee. "This provides a greater quantity of data for training and improves the performance of these systems in identifying rare conditions."

The MIMLab compared the accuracy of their augmented dataset to the original dataset when fed through their AI system and found that classification accuracy improved by 20 per cent for common conditions. For some rare conditions, accuracy improved up to about 40 per cent -- and because the synthesized X-rays are not from real individuals the dataset can be readily available to researchers outside the hospital premises without violating privacy concerns.

"It's exciting because we've been able to overcome a hurdle in applying artificial intelligence to medicine by showing that these augmented datasets help to improve classification accuracy," says Valaee. "Deep learning only works if the volume of training data is large enough and this is one way to ensure we have neural networks that can classify images with high precision."

Marit Mitchell | EurekAlert!
Further information:
http://news.engineering.utoronto.ca/training-artificial-intelligence-with-artificial-x-rays/

Further reports about: X-rays artificial dataset image processing medical diagnostics

More articles from Power and Electrical Engineering:

nachricht Record efficiency for printed solar cells
09.07.2020 | Swansea University

nachricht Bespoke catalysts for power-to-X
09.07.2020 | Karlsruher Institut für Technologie (KIT)

All articles from Power and Electrical Engineering >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: The spin state story: Observation of the quantum spin liquid state in novel material

New insight into the spin behavior in an exotic state of matter puts us closer to next-generation spintronic devices

Aside from the deep understanding of the natural world that quantum physics theory offers, scientists worldwide are working tirelessly to bring forth a...

Im Focus: Excitation of robust materials

Kiel physics team observed extremely fast electronic changes in real time in a special material class

In physics, they are currently the subject of intensive research; in electronics, they could enable completely new functions. So-called topological materials...

Im Focus: Electrons in the fast lane

Solar cells based on perovskite compounds could soon make electricity generation from sunlight even more efficient and cheaper. The laboratory efficiency of these perovskite solar cells already exceeds that of the well-known silicon solar cells. An international team led by Stefan Weber from the Max Planck Institute for Polymer Research (MPI-P) in Mainz has found microscopic structures in perovskite crystals that can guide the charge transport in the solar cell. Clever alignment of these "electron highways" could make perovskite solar cells even more powerful.

Solar cells convert sunlight into electricity. During this process, the electrons of the material inside the cell absorb the energy of the light....

Im Focus: The lightest electromagnetic shielding material in the world

Empa researchers have succeeded in applying aerogels to microelectronics: Aerogels based on cellulose nanofibers can effectively shield electromagnetic radiation over a wide frequency range – and they are unrivalled in terms of weight.

Electric motors and electronic devices generate electromagnetic fields that sometimes have to be shielded in order not to affect neighboring electronic...

Im Focus: Gentle wall contact – the right scenario for a fusion power plant

Quasi-continuous power exhaust developed as a wall-friendly method on ASDEX Upgrade

A promising operating mode for the plasma of a future power plant has been developed at the ASDEX Upgrade fusion device at Max Planck Institute for Plasma...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Contact Tracing Apps against COVID-19: German National Academy Leopoldina hosts international virtual panel discussion

07.07.2020 | Event News

International conference QuApps shows status quo of quantum technology

02.07.2020 | Event News

Dresden Nexus Conference 2020: Same Time, Virtual Format, Registration Opened

19.05.2020 | Event News

 
Latest News

X-ray scattering shines light on protein folding

10.07.2020 | Life Sciences

Looking at linkers helps to join the dots

10.07.2020 | Materials Sciences

Surprisingly many peculiar long introns found in brain genes

10.07.2020 | Life Sciences

VideoLinks
Science & Research
Overview of more VideoLinks >>>