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.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencessoftware
- medical and health sciencesclinical medicineradiology
- medical and health sciencesclinical medicineoncologybreast cancer
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social scienceseconomics and businessbusiness and managementemployment
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-SMEInst-2018-2020-1
Finanzierungsplan
SME-1 - SME instrument phase 1Koordinator
EC1V 9BG LONDON
Vereinigtes Königreich
Die Organisation definierte sich zum Zeitpunkt der Unterzeichnung der Finanzhilfevereinbarung selbst als KMU (Kleine und mittlere Unternehmen).