referenced there. This is retro-active, and authors are not required to take any extra step.
While the most visible consequence is that we are now issuing our own DOI numbers, it is also an important step toward free and open science, maintaining high-quality standards for referencing and archival of scientific works. This will facilitate the discovery and accurate citation for other researchers and cross-referencing with other publishers.
We are able to cover the additional costs associated with this change through grants and other funding, meaning that we maintain our low submission fee (USD 10) and publishing at MELBA does not cost anything to authors and readers alike.
More exciting developments are in the works, stay tuned!
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.
These techniques have numerous applications in medical imaging and analysis, including: educational purposes, dataset augmentation and imputation, correction of dataset biases, image reconstruction and synthesis problems. The Special Issue aims to collect new contributions in this general area.
Topics of interest can include, but are not limited to:
We kindly invite researchers to contribute their high-quality original articles on these topics to our Special Issue. We will be considering submissions between now and
August 31 October 1st 2023. When submitting your manuscript, the authors should use the cover letter to indicate their intention to be considered in the special issue. The editorial team will aim to streamline the reviews and return a decision within 4-6 weeks from submission. Final accepted papers will be highlighted in a special issue that will be collectively published in the fall of 2023.
Edit: The submission deadline has been moved to
August 31 October 1st 2023.
Information Processing in Medical Imaging (IPMI) is one of the longest-running conference series in medical imaging, founded in 1969. The conference has a number of traditions, ranging from unrestricted discussion time, via reading groups and a conference choir, to a soccer game. Due to the COVID-19 pandemic, the 2021 conference was held virtually, still holding on to as many traditions as possible. The conference had 150 attendees, and featured both the traditional reading groups and a far less traditional virtual conference venue to recreate the community feeling known from physical IPMI conferences as much as possible (including the soccer game!).
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. All 11 papers were accepted for the final special issue. These papers cover the topics discussed at the conference well, ranging from uncertainty estimation, via learning hyperparameter tuning, to designing and utilizing geometric priors. We are excited and thankful to present these papers in openly available form to the community through MELBA, including the recorded conference talks, which can be found from the conference website.
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.
The recordings of the first MELBA symposium are now available online: