The core focus of the Breast Cancer Imaging Lab lies in the personalization of breast screening strategies. We believe that decisions regarding screening pathways should be guided by objective features extracted from the breast images, rather than relying solely on subjective evaluations derived from radiologists’ visual assessment and professional expertise. The resulting outcome is expected to be an earlier detection of breast cancer, minimization of the occurrence of false-positive biopsies and consequently an elevation of the standard of patient care and improvement of quality of life.
Research
Extensive research is being conducted to identify the challenges and limitations in MRI diagnosis of breast cancer, paving the way for the development of innovative solutions.
Developments
The research lab focuses on developing AI solutions for breast MRI, aiming to revolutionize detection and analysis techniques in breast cancer diagnosis.
Publications
The lab actively publishes articles showcasing their research findings and advancements in the field of AI in breast MRI, contributing to scientific knowledge and dissemination of valuable insights.
Latest Publications
'Earlier than Early' detection of breast cancer
Prof. Miri Sklair-Levy, Prof. Eitan Friedman, Dr. Noam Nissan
Breast MRI Is the most sensitive modality for screening. High-risk patients are recommended to undergo a yearly MRI. In ~60% of breast cancer diagnosed patients, an abnormality was present in the previous MRI, in the same area where the tumor was later diagnosed.
For several reasons, the abnormality wasn't suspected as cancerous. An AI system can successfully classify very early breast abnormalities as cancerous/non-cancerous, potentially allowing an 'Earlier than Early' breast cancer detection.