MELBA – Machine Learning for Biomedical Imaging

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

Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow

2026/07/22
MELBA–BVM 2025 Special Issue

Eytan KatsInstitute of Medical Informatics, University of Luebeck, Luebeck, Germany, Kai GeisslerFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, Jochen G. HirschFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, Daniel MensingFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, Julien SenegasPhilips Research, Hamburg, Germany, Stefan HeldmanFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, Mattias P. HeinrichInstitute of Medical Informatics, University of Luebeck, Luebeck, Germany

PDF · Code

Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains

2026/07/02
July 2026 issue

Iman IslamSchool of Biomedical Engineering & Imaging Sciences, King’s College London, UK, Bram RuijsinkSchool of Biomedical Engineering & Imaging Sciences, King’s College London, UK, Esther Puyol-AntónSchool of Biomedical Engineering & Imaging Sciences, King’s College London, UK, Andrew J. ReaderSchool of Biomedical Engineering & Imaging Sciences, King’s College London, UK, Andrew P. KingSchool of Biomedical Engineering & Imaging Sciences, King’s College London, UK

PDF · arXiv

Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings

2026/07/02
MELBA–BVM 2025 Special Issue

PDF · arXiv

More publications...

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.

Deadline extended: April 21, 2025. More details: https://faimi-workshop.github.io/2024-melba/

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.

Zoom link: https://cornell.zoom.us/j/97915132810?pwd=b21TNmVDbzJURWcrSUlNcHdrU2Vydz09
Meeting ID: 979 1513 2810
Passcode: 115605