We are pleased to announce that in collaboration with MELBA (The Journal of Machine Learning for Biomedical Imaging), we encourage the submission of manuscripts on the topic of Fairness of AI in Medical Imaging. MELBA 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.
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 aim 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 kindly invite 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 will be required to be a reviewer for the special issue. We will be considering submissions between now and 31st of March, 2025, although early submissions are encouraged. Submissions should follow the author guidelines for the MELBA journal. The submissions will be reviewed on a rolling basis, i.e., the review process will start once your paper is received. Papers will be 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