Communications of the ACM
Tau Uses 'deep Learning' to Assist Overburdened Diagnosticians
Researchers at Tel Aviv University have developed tools to facilitate computer-assisted diagnosis of medical images in x-rays, computed tomography scans, and magnetic resonance imaging.
Credit: American Friends of Tel Aviv University
Tel Aviv University (TAU) researchers have developed a range of tools to facilitate the computer-assisted diagnosis of x-rays, computed tomographic (CT) scans, and magnetic resonance imaging (MRI).
The researchers say the new system will enable radiologists to attend to complex cases that require their full attention, without spending as much time on simpler cases. "Our goal is to use computer-assisted 'Deep Learning' technologies to differentiate between healthy and non-healthy patients, and to categorize all pathologies present in a single image through an efficient and robust framework that can be adapted to a real clinical setting," says TAU professor Hayit Greenspan.
The researchers want to use deep learning to develop diagnostic tools for the automated detection and labeling of pathologies in radiographic images. They have already developed the deep-learning technology to support automated chest x-ray pathology identification, liver lesion detection, MRI lesion analysis, and other tasks. "Such systems can improve accuracy and efficiency in both basic and more advanced radiology departments around the world," Greenspan says.
The system is based on transfer learning, in which networks originally trained on regular images are used to categorize medical images. Greenspan notes the features and parameters that represent millions of general images provide a good signature for the analysis of medical images as well.
From American Friends of Tel Aviv University
View Full Article
Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA
No entries found