Project description
Generating synthetic 3D mammography images from 2D mammograms
Breast cancer is a significant global health issue, being the most common cancer among women worldwide. While 3D screening mammograms can more effectively detect potential tumours than their traditional 2D counterparts, access to advanced 3D imaging technologies is limited in low-resource regions. With the support of the Marie Skłodowska-Curie Actions programme, the BRIDGE project will introduce a novel AI framework that can produce synthetic 3D mammography and/or magnetic resonance images from standard 2D mammograms. Using advanced machine learning techniques like Swin Transformer and generative adversarial networks, BRIDGE will develop a system that can generate clinically actionable 3D images. The approach will then be validated in clinical trials through collaboration with radiologists and oncologists in low-income countries.
Objective
BRIDGE (Breast Radiography Image Dimension Generation and Enhancement) is an innovative research project aimed at revolutionising breast cancer screening in low-resource settings. Despite being the most frequently diagnosed cancer in women worldwide, with alarming mortality rates, access to advanced 3D imaging technologies remains limited in many regions. BRIDGE proposes to bridge this gap by developing a novel artificial intelligence (AI) framework to generate synthetic 3D mammography images and/or magnetic resonance images (MRI) from standard 2D mammograms. Further, it aims to detect the presence of anomalies in the segmented 3D images.
Leveraging state-of-the-art deep learning techniques, including Swin Transformers and Generative Adversarial Networks, BRIDGE will create a robust system capable of producing clinically actionable 3D breast images. This approach will enhance diagnostic capabilities in resource-limited settings without the need for expensive 3D imaging equipment, potentially improving early detection rates and patient outcomes. Additionally, the GAN method is utilized to detect anomalies in 3D images.
The project's interdisciplinary methodology integrates expertise from AI, medical imaging, clinical practice, and ethics. It focuses on four key areas: cross-modality synthesis, clinical applicability, resource optimisation, and ethical AI development. By collaborating with radiologists and oncologists of low-income countries, BRIDGE ensures that the generated images not only are visually accurate but also enhance clinical decision-making.
BRIDGE aims to validate its approach through rigorous clinical trials and cost-effectiveness analyses. The project's success could significantly narrow the global disparity in breast cancer diagnostics, contributing to improved healthcare equity worldwide. Moreover, this research will advance the field of synthetic medical imaging, potentially opening new avenues for AI applications in healthcare.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences sociology demography mortality
- medical and health sciences clinical medicine oncology breast cancer
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- engineering and technology medical engineering diagnostic imaging
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08007 BARCELONA
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.