Ron Kimmel, PhD

About the Investigator:

Prof. Kimmel’s main research interests lie in the realms of computer vision, machine learning, medical imaging, and scientific computation. He received his BSc, MSc, and DSc/PhD degrees from The Technion, and then performed postdoctoral research at Lawrence Berkeley National Laboratory and the Department of Mathematics at the University of California, Berkeley. When he returned to Israel, he founded and heads the Geometric Image Processing (GIP) Lab at The Technion, where he is presently a full Professor. His current focus is on digital pathology and computational oncology.

About the Research:

Breast cancer is the most common cancer diagnosed in women worldwide and the leading cause of cancer-related deaths among women. Women over forty are advised to have regular checkups that often include testing a biopsy sample for proteins linked to tumor growth and survival.

In recent years, the FDA has recommended using digitally scanned biopsy slides for clinical evaluations. Digital systems, particularly those using deep learning, have significantly improved many aspects of life, including medical diagnostics. Prof. Kimmel believes that AI systems, which can analyze large amounts of data from clinical trials, will enhance prognostic evaluations and help doctors choose better treatments. AI systems are often more accurate than human observations and decision-making.

The Kimmel team proposes to test an AI system to improve the consistency, relevance, efficiency, and accuracy of cancer subtyping, which should help tailor treatments to individual patients. The system will require only a digital scanner and internet access, making advanced analysis of biopsy samples possible in any clinic, even in areas with limited medical resources. The results of this study should eventually lead to enhanced pathological methods, better prognostic tools for oncologists, and should ultimately improve the quality of life for patients worldwide.