TAU engineers solution for overworked hospital staff

Prof. Greenspan discussed her lab's plan to implement "Deep Learning," a new area of Machine Learning research that harnesses artificial intelligence for various scientific fields, at the Israeli Symposium on Computational Radiology held at TAU last December. Her goal is to use Deep Learning to develop diagnostic tools for the automated detection and labelling of pathologies in radiographic images.

Prof. Greenspan's lab is one of only a few labs in the world dedicated to the application of Deep Learning in medicine. She and her team have already developed the technology to support automated chest X-ray pathology identification using Deep Learning, liver lesion detection, MRI lesion analysis and other tasks.

"We have developed tools to support decision-making in radiology with computer vision and machine learning algorithms. This will help radiologists make more accurate, more quantitative and more objective decisions," said Prof. Greenspan. "This is especially crucial when it comes to initial screenings. Such systems can improve accuracy and efficiency in both basic and more advanced radiology departments around the world."

Prof. Greenspan is also exploring the use of "transfer learning" in her research on the medical applications of Deep Learning. "Crowdsourcing was essential for the application of Deep Learning on general image searches such as Google search," said Prof. Greenspan. "But when it comes to medical imaging, there are privacy issues and there’s very little comprehensive data available at this point.

"In 'transfer learning,' we use networks originally trained on regular images to categorize medical images. The features and parameters that represent millions of general images provide a good signature for the analysis of medical images as well."

Prof. Greenspan's work is supported by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI) and the Israeli Finance Ministry, in collaboration with Sheba Medical Center. She is also head co-editor of a special issue on "Deep Learning in Medical Imaging," which will be published in the journal IEEE Transactions on Medical Imaging in May.

This article was originally published by AFTAU.