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Funded Projects › H2020

MAMMO1 · Deep learning for mammography: Improving accuracy and productivity in breast cancer diagnosis.

H2020Status: CLOSED1 January 201930 April 2019EU funding €50,000Call H2020-EIC-SMEInst-2018-2020

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.

Consortium · 1 organisation

coordinator

KHEIRON MEDICAL TECHNOLOGIES LTD

UK · €50,000

Research fields

View the official record on CORDIS →

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Source: CORDIS, Publications Office of the European Union. Global Research Partnerships surfaces open EU research data to help you find collaborators; we are not affiliated with the European Union.