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
Knowledge-based anomaly detection for identifying network-induced shape artifacts
2026/09/29MIDL 2025 special issue
Rucha DeshpandedDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Tahsin RahmandDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Miguel LagodDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Adarsh SubbaswamydDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Jana G. DelfinodDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Ghada ZamzmidDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Elim ThompsondDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Aldo BadanodDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA, Seyed KahakiDivision of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U. S. Food and Drug Administration, USA
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
2026/09/28UNSURE2025 special issue
Gonzalo Esteban Mosquera RojasDepartment of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands, Sebastian R. van der VoortDepartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands, Carolin M. PirklGE HealthCare, Munich, Germany, Sandeep KaushikGE HealthCare, USA, Marion SmitsDepartment of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
Brain tumor Centre, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
Medical Delta, Delft, the Netherlands, Stefan KleinDepartment of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation
2026/09/24UNSURE2025 special issue
Gabriel Oliveira-StahlHawkes Institute, Department of Computer Science, University College London, UK
Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK, Anna SchroderHawkes Institute, Department of Computer Science, University College London, UK
Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK, James MoggridgeLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, UK, Hamza A. SalhabLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK, Caroline MicallefLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK, Josephine BarnesDementia Research Centre, University College London, UK, M. Jorge CardosoSchool of Biomedical Engineering & Imaging Sciences, King’s College London, UK, Carole H. Sudre*Hawkes Institute, Department of Computer Science, University College London, UK
Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, UK
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK, Matthew Grech-Sollars*Hawkes Institute, Department of Computer Science, University College London, UK
Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
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