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

Obiettivo

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.

Invito a presentare proposte

H2020-EIC-SMEInst-2018-2020

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Bando secondario

H2020-SMEInst-2018-2020-1

Meccanismo di finanziamento

SME-1 - SME instrument phase 1

Coordinatore

KHEIRON MEDICAL TECHNOLOGIES LTD
Contribution nette de l'UE
€ 50 000,00
Indirizzo
2ND STYLUS BUILDING, 112-116 OLD STREET
EC1V 9BG LONDON
Regno Unito

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PMI

L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.

Regione
London Inner London — East Haringey and Islington
Tipo di attività
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Collegamenti
Costo totale
€ 71 429,00