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MAMMO1 SIGNED

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

Total Cost €

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EC-Contrib. €

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Partnership

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Project "MAMMO1" data sheet

The following table provides information about the project.

Coordinator
KHEIRON MEDICAL TECHNOLOGIES LTD 

Organization address
address: ROCKETSPACE, 40 ISLINGTON HIGH STREET
city: LONDON
postcode: N1 8EQ
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website https://www.kheironmed.com/meet-mia
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-1
 Funding Scheme SME-1
 Starting year 2019
 Duration (year-month-day) from 2019-01-01   to  2019-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KHEIRON MEDICAL TECHNOLOGIES LTD UK (LONDON) coordinator 50˙000.00

Map

 Project 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.

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The information about "MAMMO1" are provided by the European Opendata Portal: CORDIS opendata.

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