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

Ziel

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.

Aufforderung zur Vorschlagseinreichung

H2020-EIC-SMEInst-2018-2020

Andere Projekte für diesen Aufruf anzeigen

Unterauftrag

H2020-SMEInst-2018-2020-1

Koordinator

KHEIRON MEDICAL TECHNOLOGIES LTD
Netto-EU-Beitrag
€ 50 000,00
Adresse
2ND STYLUS BUILDING, 112-116 OLD STREET
EC1V 9BG LONDON
Vereinigtes Königreich

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KMU

Die Organisation definierte sich zum Zeitpunkt der Unterzeichnung der Finanzhilfevereinbarung selbst als KMU (Kleine und mittlere Unternehmen).

Ja
Region
London Inner London — East Haringey and Islington
Aktivitätstyp
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Gesamtkosten
€ 71 429,00