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

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.

Call for proposal

H2020-EIC-SMEInst-2018-2020

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Sub call

H2020-SMEInst-2018-2020-1

Coordinator

KHEIRON MEDICAL TECHNOLOGIES LTD
Net EU contribution
€ 50 000,00
Address
2nd Stylus Building, 112-116 Old Street
EC1V 9BG London
United Kingdom

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Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Non-EU contribution
€ 21 429,00