Breast cancer is the most commonly diagnosed cancer in women and the second leading cause of cancer deaths among them. X-ray mammography screening is an effective tool for reducing mortality among women, but the high volume of mammograms generated every year, combined with the visual challenge of identifying subtle abnormalities in a complex background, make mammograms analysis a difficult task. Currently, radiologists lack an effective tool to help them in this task.

This project proposes an AI-based system for the rapid and automated detection of breast cancers by an autonomous analysis of straightforward mammograms, keeping the radiologists focused on the difficult cases where their expertise is invaluable.

The proposed solution will lead to shorter analysis time, less missed diagnosis, and to a decreased number of unnecessary call-backs, resulting in a broader adhesion to mammography screening programs, marked reduction in women breast cancer mortality, and significant cost savings for the society. Additionally, this project will bring forward the research in the field of AI for medical imaging, potentially leading to innovation for the treatment of other types of cancer.