Machine Learning for Biomedical Imaging
Welcome to Melba (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
MICCAI 2023 LNQ challenge special issue
Reuben DorentBrigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, Roya KhajaviBrigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, Tagwa IdrisMassachusetts General Hospital, Harvard Medical School, Boston, MA, USA, Erik ZieglerYunu, Inc., Cary, NC, USA, Bhanusupriya SomarouthuMassachusetts General Hospital, Harvard Medical School, Boston, MA, USAReuben DorentBrigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA et al.
Yunu, Inc., Cary, NC, USA, Heather JaceneBrigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Dana-Farber Cancer Institute, Boston, MA, USA, Ann LaCasceDana-Farber Cancer Institute, Boston, MA, USA, Jonathan DeisslerDivision of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Jan EhrhardtInstitute of Medical Informatics, University of Lübeck, Lübeck, Germany
German Research Center for Artificial Intelligence, Lübeck, Germany, Sofija EngelsonInstitute of Medical Informatics, University of Lübeck, Lübeck, Germany, Stefan M. FischerTechnical University Munich, Munich, Germany
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany
Munich Center of Machine Learning (MCML), Munich, Germany, Yun GuInstitute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, Heinz HandelsInstitute of Medical Informatics, University of Lübeck, Lübeck, Germany
German Research Center for Artificial Intelligence, Lübeck, Germany, Satoshi KasaiNiigata University of Health and Welfare, Niigata, Japan, Satoshi KondoMuroran Institute of Technology, Hokkaido, Japan, Klaus Maier-HeinDivision of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
University of Heidelberg, Heidelberg, Germany, Julia A. SchnabelTechnical University Munich, Munich, Germany
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany
Munich Center of Machine Learning (MCML), Munich, Germany
School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK, Guotai WangUniversity of Electronic Science and Technology of China, Chengdu, China
Shanghai AI laboratory, Shanghai, China, Litingyu WangUniversity of Electronic Science and Technology of China, Chengdu, China, Tassilo WaldDivision of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
University of Heidelberg, Heidelberg, Germany, Guang-Zhong YangInstitute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, Hanxiao ZhangInstitute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, Minghui ZhangInstitute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, Steve PieperIsomics Inc, Cambridge, MA, USA, Gordon HarrisMassachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Yunu, Inc., Cary, NC, USA, Ron KikinisBrigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, Tina KapurBrigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
UNSURE2023 special issue
Jacob J. PeoplesSchool of Computing, Queen’s University, Kingston, ON, Canada, Mohammad HamghalamSchool of Computing, Queen’s University, Kingston, ON, CanadaJacob J. PeoplesSchool of Computing, Queen’s University, Kingston, ON, Canada et al.
Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran, Imani JamesDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Maida WasimDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Natalie GangaiDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Hyunseon Christine KangDepartment of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, X. John RongDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, Yun Shin ChunDepartment of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, Richard K. G. DoDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA, Amber L. SimpsonSchool of Computing, Queen’s University, Kingston, ON, Canada
Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada
Latest news
2024/05/14 – MELBA Symposium on Generative Models
We are thrilled to announce the MELBA Symposium on Generative Models, which will take place on Tuesday, June 11 at 9-11:30 AM EDT, 3-5:30 PM CEST! Join us for an exciting lineup of talks from spotlight papers at MELBA surrounding generative models, machine learning and biomedical imaging. Afterwards, there will be a panel discussion with all speakers moderated by a member of the MELBA board.
Meeting ID: 979 1513 2810
Passcode: 115605
2024/02/02 – Journal-to-conference track at MIDL 2024
The MELBA board happy to announce that, in partnership with the MIDL foundation, MIDL 2024 will feature a "journal-to-conference" track, inviting recently published* MELBA papers to present their work during the conference. Going forward newly published** papers will have the possibility to present their work at MIDL.
Moreover, the best papers of the conference will be invited to submit a journal extension to a special issue in the MELBA journal.
This will strenghten the ongoing partnership between the MIDL foundation and MELBA board, both of which are strongly committed to Open Science and Open Access.
* This excludes papers that are already an extension of a conference/workshop paper.
** Registration deadline will be communicated at a later date.
2023/05/24 – MELBA special issue on image registration
Motion estimation, shape matching and multimodal fusion are some of the core applications in medical image registration that have seen substantial progress through the advent of deep learning-based technique over recent years. We invite you to submit full papers or extended conference works that extend the knowledge in this emerging research field. Submissions are invited in all areas of learning-based medical image registration.
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 Oct 1, 2023 November 30 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/winter of 2023.
In conjunction with MIDL 2024 in Paris we are planning to provide an automatic presentation slot for all original papers accepted within this special issue if the authors are interested in showcasing their research to this audience as well.
Guest Editors:
- Dr. Veronika Zimmer, TU München, Germany;
- Dr. Alessa Hering, RadboudUMC, Netherlands;
- Dr. Mattias Heinrich, University of Lübeck, Germany.
Edit: The submission deadline has been extended from October 1 to November 30, 2023.