Neuradicon: Operational representation learning of neuroimaging reports
Neuradicon allows the segmentation, analysis, classification, representation and interrogation of neuroradiological reports structure and content. It offers a blueprint for the extraction of rich, quantitative, actionable signals from unstructured text data in an operational context.
Authors
- Henry Watkins (ORCID: https://orcid.org/0000-0001-6330-6195)
- Parashkev Nachev (ORCID: https://orcid.org/0000-0002-2718-4423)
- James Teo (ORCID: https://orcid.org/0000-0002-6899-8319)
- Yee H. Mah (ORCID: https://orcid.org/0000-0002-6652-9922)
- Walter Hugo Lopez Pinaya (ORCID: https://orcid.org/0000-0003-3739-1087)
- Geraint Rees (ORCID: https://orcid.org/0000-0002-9623-7007)
- M. Jorge Cardoso (ORCID: https://orcid.org/0000-0003-1284-2558)
- Sébastien Ourselin (ORCID: https://orcid.org/0000-0002-5694-5340)
- Holger Engleitner
- Robert Gray (ORCID: https://orcid.org/0000-0001-5035-4999)
- Ashwani Jha (ORCID: https://orcid.org/0000-0003-2835-6837)
- Paul Wright (ORCID: https://orcid.org/0000-0001-5297-6257)
- R.J. Jaeger
- Adam Julius
Institutions
- Queen Mary University of London (GB)
- King's College London (GB)
- National Hospital for Neurology and Neurosurgery (GB)
- University College London (GB)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108638
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- Medical Research Council
- Engineering and Physical Sciences Research Council