Project description
Analyzing and mitigating hidden bias in AI-based leukemia diagnostics
Despite their impressive performance, AI tools used in leukaemia diagnostics may carry hidden biases based on age, sex, or other patient factors. These can compromise accuracy and fairness. What’s more, the risks remain poorly understood, particularly in powerful foundation models trained on vast image datasets. The ERC-funded LeukoBIAS project will develop methods to detect and reduce bias in AI-driven blood cancer diagnostics. Using data from over 6 000 real-world patients, the project will apply advanced machine learning techniques to uncover and correct unfair model behaviour. Beyond improving leukaemia diagnostics, LeukoBIAS also aims to influence broader AI regulation and scalability across other medical domains, aligning with the EU’s push for trustworthy, equitable AI in healthcare.
Objective
Foundation models have revolutionized image processing in healthcare, offering robust performance across various tasks without task-specific training. However, potential biases in these models, especially when applied to critical medical diagnostics such as leukemia detection, remain largely unexplored. LeukoBIAS aims to address this crucial gap by developing a framework for analyzing and mitigating bias in foundation model-based leukemia detection algorithms.
Building on my previous work in AI-driven leukemia diagnostics, we will leverage a unique real-world dataset of over 6000 patients from my long-time industry partner, the Munich Leukemia Laboratory, to investigate biases related to sex, age, and other patient characteristics. Our approach combines advanced machine learning techniques, including multiple instance learning and attention mechanisms, with novel bias detection and mitigation strategies.
The project consists of three work packages: (i) bias analysis in foundation model-based leukemia diagnostics; (ii) development of bias mitigation techniques for model fine-tuning; and (iii) exploration of intellectual property and commercialization opportunities for bias auditing.
The innovative potential of the project extends beyond leukemia diagnostics. We will thus explore the scalability of our approach to other modalities and conduct a comprehensive market analysis to identify potential industry partners. LeukoBIAS will contribute to the scientific understanding of bias in medical AI and pave the way for more equitable and reliable AI-driven diagnostic tools. By addressing the requirements outlined in the EU Artificial Intelligence Act, LeukoBIAS is poised to have a significant impact on the development and deployment of trustworthy AI in healthcare. By providing a framework for bias analysis and mitigation, this project will contribute to more accurate diagnoses, improved patient outcomes, and accelerated innovation in AI-driven medicine.
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.
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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.1.1 - European Research Council (ERC)
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Topic(s)
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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-ERC-POC - HORIZON ERC Proof of Concept Grants
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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) ERC-2024-POC
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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.
85764 Neuherberg
Germany
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