HAMAM (Highly accurate breast cancer diagnosis through integration of biological knowledge, novel imaging modalities, and modelling) project will tackle this challenge by providing a means to seamlessly integrate the available multi-modal images and the patient information on a single clinical workstation. Based on knowledge gained from a large multi-disciplinary database, populated within the scope of this project, suspicious breast tissue will be characterised and classified.
HAMAM will achieve this by:
- Building the tools needed to integrate datasets / modalities into a single interface.
- Providing pre processing / standardization tools that will allow for optimal comparison of disparate data
- Building spatial correlation information datasets to allow for new similarity and multimodal tissue models. These will be key in the detection and diagnosis of breast cancer
- Building in adaptability that allows for the integration of other sources of knowledge such as tumour models, genetic data, genotype, phenotype and standardised imaging.
The exact diagnosis of suspicious breast tissue is ambiguous in many cases. HAMAM will resolve this using the statistical knowledge extracted from the large case database. The clinical workstation will suggest additional image modalities that may be captured to optimally resolve these uncertainties. The workstation thus guides the clinician in establishing a patient specific optimal diagnosis. This ultimately leads to a more specific and individual diagnosis.
For further information, please visit:
EIBIR gemeinnuetzige GmbH zur Foerderung der. Erforschung der biomedizinischen Bildgebung
- Boca Raton Community Hospital Inc (USA)
- MeVis Research GmbH (Germany)
- MeVis Medical Solutions AG (Germany)
- University College London (United Kingdom)
- Radboud Universiteit Nijmegen - Stichting Katholieke Universiteit (Netherlands)
- Charité - Universitätsmedizin Berlin (Germany)
- The University of Dundee (United Kingdom)
- Eidgenössische Technische Hochschule Zürich (Switzerland)
Timetable: from 09/2008 - to 08/2011
Total cost: 4.250.000
EC funding: 3.100.000
Programme Acronym: FP7-ICT
Subprogramme Area: Virtual physiological human
Contract type: Collaborative project (generic)
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