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.
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
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
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Robin CamarasaErasmus MC, Rotterdam, Daniel BosErasmus MC, Rotterdam, Jeroen HendrikseUniversity Medical Center Utrecht, Paul NederkoornAcademic Medical Center University of Amsterdam, M. Eline Kooi CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Aad van der LugtErasmus MC, Rotterdam, Marleen de BruijneErasmus MC, Rotterdam
University of Copenhagen, Denmark
Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization
Franz ThalerMedical University of Graz, Austria, Christian PayerGraz University of Technology, Austria, Martin UrschlerThe University of Auckland, New Zealand, Darko ŠternMedical University of Graz, Austria
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: