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 statement of the journal, or jump right away to the journal publications. For authors, instructions are available here.
Latest publications
March 2023 issue
Jérôme RonyLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada, Soufiane BelharbiLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada, Jose DolzLIVIA, Dept. of Software and IT Engineering, École de technologie supérieure, Montreal, Canada, Ismail Ben AyedLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada, Luke McCaffreyGoodman Cancer Research Centre, Dept. of Oncology, McGill University, Montreal, Canada, Eric GrangerLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada
Focused Decoding Enables 3D Anatomical Detection by Transformers
2023/02/27February 2023 issue
Bastian WittmannDepartment of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, Fernando NavarroDepartment of Informatics, Technical University of Munich, Munich, Germany, Suprosanna ShitDepartment of Informatics, Technical University of Munich, Munich, Germany, Bjoern MenzeDepartment of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
Latest news
2022/12/19 – MELBA special issue on generative models
MELBA (The Journal of Machine Learning for Biomedical Imaging) encourages the submission of manuscripts on the general topic of “generative models for biomedical imaging and image analysis”. MELBA is 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.
In recent years, there has been a flurry of developments in machine learning (including Variational Auto-Encoders or VAEs, Generative Adversarial Networks or GANs, Normalizing Flows or NFs, and lately, Diffusion Models) that enable us to generate high-quality, realistic synthetic data such as high-dimensional images, volumes, or tensors.
2022/07/01 – MELBA-IPMI 2021 special issue
Information Processing in Medical Imaging (IPMI) is one of the longest-running conference series in medical imaging, founded in 1969. [...] IPMI 2021 received 200 valid submissions, of which 59 were accepted for publication at the conference. From these, 29 papers were invited to submit an extended journal version to the first special edition of the MELBA journal associated with an IPMI conference. Out of the invited papers, we received 11 submissions, which all underwent a new peer-review process. [...] These papers cover the topics discussed at the conference well, ranging from uncertainty estimation, via learning hyperparameter tuning, to designing and utilizing geometric priors.
2022/06/21 – RSS feed for new articles and blog-posts
We are happy to unveil a new RSS feed (available at https://www.melba-journal.org/feed.rss), that includes both research articles and blog posts.