We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.
Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Ampronix,  Inc

Tetris-Like Program Could Speed Up Breast Cancer Detection

By Medimaging International staff writers
Posted on 24 Sep 2018
Print article
Image: A three-dimensional culture of human breast cancer cells, with DNA stained blue and a protein in the cell surface membrane stained green (Photo courtesy of Tom Misteli, Ph.D., and Karen Meaburn, Ph.D / NIH IRP).
Image: A three-dimensional culture of human breast cancer cells, with DNA stained blue and a protein in the cell surface membrane stained green (Photo courtesy of Tom Misteli, Ph.D., and Karen Meaburn, Ph.D / NIH IRP).
A fully automated medical image analysis program to detect breast tumors that uses a unique style to focus on the affected area is being developed by researchers from the University of Adelaide’s Australian Institute for Machine Learning (Adelaide, South Australia, Australia). Using artificial intelligence (AI), the autonomous program in conjunction with an MRI scan employs the traversal movement and style of a retro video game to examine the breast area.

The program was created by applying deep reinforcement learning methods, a form of AI that enables computers and machines to learn how to do complex tasks without being programmed by humans. This allows the program to independently analyze breast tissue. The researchers managed to train the computer program using a relatively smaller amount of data, which poses a critical challenge in medical imaging.

“Just as vintage video game Tetris manipulated geometric shapes to fit a space, this program uses a green square to navigate and search over the breast image to locate lesions. The square changes to red in color if a lesion is detected,” said University of Adelaide PhD candidate Gabriel Maicas Suso. “Our research shows that this unique approach is 1.78 times faster in finding a lesion than existing methods of detecting breast cancer, and the results are just as accurate.”

“By incorporating machine learning into medical imaging analysis, we have developed a program that intuitively locates lesions quickly and accurately,” said Associate Professor Gustavo Carneiro from AIML. “More research is needed before the program could be used clinically. Our ultimate aim is for this detection method to be used by radiologists to complement, support and assist their important work in making a precise and quick prognosis. AI has an important role to play in the imaging medical field, the potential to use AI in this field is boundless.”

Related Links:
University of Adelaide’s Australian Institute for Machine Learning


Print article
Radcal
Italray

Channels

Copyright © 2000-2018 Globetech Media. All rights reserved.