Machine Learning for Biomedical Imaging
Welcome to Melba (Machine Learning for Biomedical Imaging), a web-based journal devoted to the free and unrestricted access of high quality articles in the broad field that bridges machine learning and biomedical imaging. There are no publication charges with MELBA: you wrote it, the community reviewed it, we publish it – no hidden charges and you own your own publication. *
* The Scholastica submission system requires a $10 charge during initial submission. However, we are actively working on removing this as well.
You can read more about the mission statement of the journal, or jump right away to the journal publications. For authors, instructions are available here.
Latest publications
November 2025 issue
Yaqian ChenDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, Hanxue GuDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, Yuwen ChenDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, Jicheng YangDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, Haoyu DongDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, Joseph Y. CaoDepartment of Radiology, Duke University, Durham, NC 27708, Adrian CamarenaDepartment of Surgery Duke University School of Medicine, Durham, NC 27708 , Christopher MantyhDepartment of Surgery Duke University School of Medicine, Durham, NC 27708 , Roy ColglazierDepartment of Radiology, Duke University, Durham, NC 27708, Maciej A. MazurowskiDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708 Yaqian ChenDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708 et al.
Department of Radiology, Duke University, Durham, NC 27708
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708
Department of Computer Science, Duke University, Durham, NC 27708
October 2025 issue
Susu SunCluster of Excellence: Machine Learning - New Perspectives for Science, University of Tübingen, Tübingen, Germany, Stefano WoernerCluster of Excellence: Machine Learning - New Perspectives for Science, University of Tübingen, Tübingen, Germany, Andreas MaierDepartment of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, Lisa M. KochHertie Institute for Artificial Intelligence in Brain Health, University of Tübingen, Tübingen, Germany
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, Christian F. BaumgartnerCluster of Excellence: Machine Learning - New Perspectives for Science, University of Tübingen, Tübingen, Germany
Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
Latest news
2025/03/28 – Special issue on Fairness of AI in Medical Imaging (FAIMI)
MELBA is excited to launch a special issue in collaboration with the FAIMI initiative, spotlighting research at the intersection of machine learning, medical imaging, and ethics.This issue invites contributions on:
- Bias assessment in ML for medical imaging
- Definitions and applicability of fairness in clinical contexts
- Healthcare inequalities and bias mitigation
- Ethical, legal, and regliatory considerations
- Causality, dataset bias, and moreWe welcome extended versions of FAIMI workshop papers and new submissions from the community.
2025/03/21 – HTML version of articles available
After staying in a beta state for some time, and leveraging the great work of tools such as LaTeXML, we are now including an HTML version of the articles directly into the paper pages. This is intended to facilitate skimming through articles, notably on phone or tablet.
2024/05/14 – MELBA Symposium on Generative Models
We are thrilled to announce the MELBA Symposium on Generative Models, which will take place on Tuesday, June 11 at 9-11:30 AM EDT, 3-5:30 PM CEST! Join us for an exciting lineup of talks from spotlight papers at MELBA surrounding generative models, machine learning and biomedical imaging. Afterwards, there will be a panel discussion with all speakers moderated by a member of the MELBA board.
Meeting ID: 979 1513 2810
Passcode: 115605