Objective
Breast cancer is one of the main causes of death among women worldwide. Early diagnosis by mammography scanning is the best way to prevent mortality, but it requires the intervention of a highly trained workforce (radiologists). While the demand for radiologists is on the rise, the supply is quickly diminishing worldwide. This leads to long waiting lists and delays in getting a diagnosis, negatively affecting quality of services and ultimately survival rates. There is a strong need for tools that help radiologists make accurate decisions on mammography images in less time. CAD-based systems were developed to address this need; however, they have very low specificity, which leads to a high number of false positives, unnecessarily increasing the recall rates, and raising doubts about their usefulness. Mammo1 will be a game-changer in the area of breast cancer diagnosis by applying ground-breaking machine learning techniques, which are able to outperform all the currently marketed CAD-based solutions and even single radiologists.
Fields of science
- natural sciencescomputer and information sciencessoftware
- medical and health sciencesclinical medicineradiology
- medical and health sciencesclinical medicineoncologybreast cancer
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social scienceseconomics and businessbusiness and managementemployment
Programme(s)
Funding Scheme
SME-1 - SME instrument phase 1Coordinator
EC1V 9BG LONDON
United Kingdom
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.