Breast cancer is the second leading cause of cancer-related deaths, and the most commonly diagnosed cancer among women across the world. As treatment options improve, early detection has an increasing impact on mortality, as earlier diagnosis can offer more options for successful intervention and therapies when the disease is still in its early stages.
Currently, digital mammography is the main imaging method of screening. Women typically undergo breast mammography every 1-2 years, depending on their familial history. The exam is then interpreted by radiologists who examine the images for the existence of a malignant finding. A second reading of mammograms by an additional radiologist has been proven to increase sensitivity and specificity. However, a lack of trained radiologists and time limitations often makes it impossible to incorporate second readers as part of the standard screening procedure in many countries.
To help close the gap of readily available “second readers” and aid radiologists in their analyses, more and more systems are turning to computational models based on deep neural networks to analyze healthcare data. However, the efficacy of traditional computational models in breast radiology is still a matter of debate.
In this lecture, we would explore how we collaborated with radiologists and clinicians in developing an AI system trained on a large set of digital mammography images and electronic health records from the Israel’s health care system. How this model performed in comparison to radiologists; how we make sure that its results could be trusted; and what could be some of its immediate applications.
Michal is a researcher in the Centre for Applied Research in IBM Australia, focusing on data & AI.
Prior to that she was part of the Machine Learning for Healthcare and Life Sciences group at IBM Research in Haifa, Israel. Through her work, she led multi-disciplinary teams in projects aiming towards early detection of breast and liver cancer. Michal has completed her PhD in Computer Science and Computation Biology at The Hebrew University of Jerusalem, as a 2014 Google Anita Borg and a 2016 Grace Hopper scholar. Additionally, Michal is a social entrepreneur, who co-founded a nation-wide initiative to encourage girls to study Computer Science in high school and beyond.
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