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
October 2026 issue
Zhenhao GuoNew York University, New York, NY 10012, USA, Rachit SalujaCornell Tech, New York, NY 10044, USA, Tianyu ShiSichuan University, Chengdu, 610207, CN, Tianyuan YaoVanderbilt University, Nashville, TN, 37235, USA, Quan LiuVanderbilt University, Nashville, TN, 37235, USA, Junchao ZhuVanderbilt University, Nashville, TN, 37235, USA, Haibo WangCarnegie Mellon University, Pittsburgh, PA 15213, USA, Daniel ReisenbüchlerUniversity of Regensburg, Regensburg, Bavaria 93053, DE, Haichun YangVanderbilt University Medical Center, Nashville, TN, 37232, USA, Yuankai HuoVanderbilt University, Nashville, TN, 37235, USA, Benjamin LiechtyWeill Cornell Medicine, New York, NY 10065, USA, David J. PisapiaWeill Cornell Medicine, New York, NY 10065, USA, Kenji IkemuraWeill Cornell Medicine, New York, NY 10065, USA, Steven SalvatoreeWeill Cornell Medicine, New York, NY 10065, USA, Surya SeshaneWeill Cornell Medicine, New York, NY 10065, USA, Mert R. SabuncuCornell Tech, New York, NY 10044, USAZhenhao GuoNew York University, New York, NY 10012, USA et al.
Weill Cornell Medicine, New York, NY 10065, USA, Yihe YangWeill Cornell Medicine, New York, NY 10065, USA
Northwell Health, New Hyde Park, NY 11040, USA, Ruining DengVanderbilt University, Nashville, TN, 37235, USA
Weill Cornell Medicine, New York, NY 10065, USA
Knowledge-based anomaly detection for identifying network-induced shape artifacts
2026/09/29MIDL 2025 special issue
Rucha DeshpandeDivision 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 RahmanDivision 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 LagoDivision 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 SubbaswamyDivision 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. DelfinoDivision 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 ZamzmiDivision 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 ThompsonDivision 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 BadanoDivision 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
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