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.

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:

  • new generative modeling methods that are motivated by or applicable to known problems in biomedical imaging, with strong theoretical justification and appropriate empirical evaluation;
  • empirical accounts that thoroughly evaluate, and/or compare existing generative modeling methods for biomedical imaging applications of interest; and
  • presentation of a novel biomedical imaging application, where existing generative models offer a key solution.

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 July 1, 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.