Machine Learning for Biomedical Imaging
Welcome to Melba (The Journal of 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.
You can read more about the mission statements of the journal, or jump right away to the journal publications. For authors, instructions are available here.
Latest publications
Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data
2021/10/27
Ayaan HaqueSaratoga High School, Saratog, Abdullah-Al-Zubaer ImranStanford University, Stanford, Adam WangStanford University, Stanford, Demetri TerzopoulosUniversity of California, Los Angeles
VoxelCloud, Inc., Los Angeles
Daniel GrzechImperial College London, UK, Mohammad Farid AzampourImperial College London, UK
Technische Universität München, Germany
Sharif University of Technology, Tehran, Iran, Huaqi QiuImperial College London, UK, Ben GlockerImperial College London, UK, Bernhard KainzImperial College London, UK
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, Loïc Le FolgocImperial College London, UK
Elisa ChotzoglouImperial College London, UK, Thomas DayKing’s College London, UK, Jeremy TanImperial College London, UK, Jacqueline MatthewKing’s College London, UK, David LloydKing’s College London, UK, Reza RazaviKing’s College London, UK, John SimpsonKing’s College London, UK, Bernhard KainzImperial College London, UK
PDFLatest news
2021/10/22 – New website!
We are excited and proud to unveil the new Melba website, which will take the place of the old Scholastica one.
2021/09/24 – MELBA-MIDL 2020 special issue
We are pleased to announce the first MELBA special issue on selected papers from the Medical Imaging with Deep Learning (MIDL). All papers can be found on the MELBA website: https://new.melba-journal.org/issues/midl20.html
2020/12/11 – MELBA publishes its first article!
We are pleased to announce that MELBA has published its first article: “An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation”. This paper presents an extension of the authors’ MIDL 2020 paper and is included in the MIDL 2020 Special Issue. A video presentation of the work can be found here:
https://youtu.be/VwF0KeALAFU