Mission Statement

Melba (The Journal of Machine Learning for Biomedical Imaging) 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. Melba’s aims include:


Scope

Melba (The Journal of Machine Learning for Biomedical Imaging) invites the submission of previously unpublished journal-length papers that report research developments at the interface of machine learning and biomedical imaging. Papers focusing on innovative methods and/or novel biomedical applications are strongly encouraged to be submitted.

Topics of interest include:

Overall, Melba aims to publish high-quality research contributions that add value and move science forward, including negative results that can be generalized from. Melba encourages authors to share and support their code and data, with an emphasis on replicability.

Manuscripts must communicate their ideas and findings in the English language, in a concise and complete manner. Therefore papers should be carefully proofread and polished by authors. If these criteria are not met, this can be grounds for rejecting the paper without a review.

Melba adheres to standard practices in modern scientific publication. This means that all claims need to be articulated precisely and justified through theoretical arguments and/or empirical evidence. Prior literature should be properly acknowledged. Submitted papers need to be original and not published. Significantly extended versions of conference proceedings are allowed to be submitted, however the overlap of content should be less than 50%.