The MELBA special issue on Fairness of AI in Medical Imaging is now complete. The issue brings together 9 papers on fairness in medical imaging and biomedical AI, covering topics including utility-fairness trade-offs, bias mitigation, demographic bias analysis, calibration, shortcut learning, and fairness evaluation across applications such as cardiac MRI, brain MRI, pulmonary nodule detection, lung cancer screening, skin cancer detection, and ECG classification.
Read the full special issue on the MELBA website:
View the FAIMI special issue in MELBA
MELBA (The Journal of Machine Learning for Biomedical Imaging) is a web-based journal devoted to the free and unrestricted access of high quality articles in the broad field that bridges machine learning (ML) and biomedical imaging.
Call for papers (closed)
Over the past several years, research on fairness, equity, and accountability in the context of machine learning has extensively demonstrated ethical risks in the deployment of machine learning systems in critical infrastructure, such as medical imaging. With this special issue, we aimed to encourage and emphasize research on and discussion of fairness of AI within the medical imaging domain. Topics include but are not limited to:
- Assessment of bias in ML applications for medical imaging
- Healthcare inequalities and/or the role of ML in addressing these
- Discussion of definitions of fairness in medical contexts
- Discussion of applicability of fairness in medical contexts
- Bias mitigation strategies for ML/medical imaging
- Ethics of fairness for medical imaging and medicine
- Legal/regulatory considerations of fairness
- Causality and fairness in medical imaging
- The interplay of algorithmic and dataset bias
We invited both authors of FAIMI workshop papers to submit extended versions of their papers, and other researchers working on these topics to submit their work. Per submitted paper, at least one paper author was required to be a reviewer for the special issue. Submissions were considered until 31st of March, 2025. Submissions followed the author guidelines for the MELBA journal. Submissions were reviewed on a rolling basis, i.e., the review process started once each paper was received. Papers were immediately published online upon acceptance by the editorial team:
- Veronika Cheplygina, IT University Copenhagen (contact editor)
- Aasa Feragen, DTU Compute, Technical University of Denmark
- Andrew King, King’s College London
- Ben Glocker, Imperial College London
- Enzo Ferrante, CONICET, Universidad Nacional del Litoral
- Eike Petersen, Fraunhofer Institute for Digital Medicine MEVIS, Germany
- Esther Puyol-Antón, HeartFlow and King’s College London
- Melanie Ganz-Benjaminsen, University of Copenhagen & Neurobiology Research Unit, Rigshospitalet