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
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
2022/06/29
Sebastian G. PopescuImperial College London, David J. SharpImperial College London, James H. ColeUniversity College London, Konstantinos KamnitsasImperial College London, Ben GlockerImperial College London
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Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
2022/06/23
Nalini M. SinghMassachusetts Institute of Technology, Juan Eugenio IglesiasMassachusetts General Hospital
Harvard Medical School
University College London
Massachusetts Institute of Technology, Elfar AdalsteinssonMassachusetts Institute of Technology, Adrian V. DalcaMassachusetts General Hospital
Harvard Medical School
Massachusetts Institute of Technology, Polina GollandMassachusetts Institute of Technology
Kristen M. CampbellUniversity of Utah, Salt Lake City, Haocheng DaiUniversity of Utah, Salt Lake City, Zhe Su University of California Los Angeles, Martin BauerFlorida State University, Tallahassee, P. Thomas FletcherUniversity of Virginia, Charlottesville, Sarang C. JoshiUniversity of Utah, Salt Lake City
PDF CodeVideoLatest news
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.
2022/05/27 – Recordings of the first MELBA symposium
The recordings of the first MELBA symposium are now available online.
2022/03/19 – First MELBA Symposium
Mark your calendars! We will have the first virtual MELBA Symposium on May 5, 2022, between 9a-11a US EDT.
Two papers, selected by a readers’ vote, will be presented and discussed in detail:
- Raumanns, R., Schouten, G., Joosten, M., Pluim, J. P., & Cheplygina, V. ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification;
- Quiros, A. C., Murray-Smith, R., & Yuan, K. PathologyGAN: Learning deep representations of cancer tissue.