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

Objetivo

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.

Convocatoria de propuestas

H2020-EIC-SMEInst-2018-2020

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Convocatoria de subcontratación

H2020-SMEInst-2018-2020-1

Régimen de financiación

SME-1 - SME instrument phase 1

Coordinador

KHEIRON MEDICAL TECHNOLOGIES LTD
Aportación neta de la UEn
€ 50 000,00
Dirección
2ND STYLUS BUILDING, 112-116 OLD STREET
EC1V 9BG LONDON
Reino Unido

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Pyme

Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.

Región
London Inner London — East Haringey and Islington
Tipo de actividad
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Enlaces
Coste total
€ 71 429,00