Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning
Multi-centric prostate MRI studies were used for sequence annotation model training. Automatic sequence annotation requires few instances and generalises temporally. Sequence annotation, necessary for clinical AI model training, can be performed automatically.
Authors
- Carlos Bilreiro (ORCID: https://orcid.org/0000-0001-7755-0842)
- Inês Santiago (ORCID: https://orcid.org/0000-0001-7711-3669)
- Kostas Marias (ORCID: https://orcid.org/0000-0003-3783-5223)
- Nickolas Papanikolaou (ORCID: https://orcid.org/0000-0003-3298-2072)
- Celso Matos (ORCID: https://orcid.org/0000-0001-7383-6309)
- Daniele Regge (ORCID: https://orcid.org/0000-0001-8267-5279)
- Ana Sofia Castro Verde (ORCID: https://orcid.org/0000-0001-6729-5681)
- José Guilherme de Almeida (ORCID: https://orcid.org/0000-0002-1887-0157)
- Manolis Tsiknakis (ORCID: https://orcid.org/0000-0001-8454-1450)
- Joana Ip (ORCID: https://orcid.org/0000-0002-5297-4515)
Institutions
- Champalimaud Foundation (PT)
- Hellenic Mediterranean University (GR)
- Royal Marsden Hospital (GB)
- FORTH Institute of Electronic Structure and Laser (GR)
- FORTH Institute of Applied and Computational Mathematics (GR)
- Candiolo Cancer Institute (IT)
- University of Turin (IT)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1186/s13244-025-01938-2
- Citations
- 1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Horizon 2020