An AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels

Niamh Belton, Aonghus Lawlor, Kathleen M. Curran

Open source

DOI
10.1016/j.artmed.2025.103138
Published
2025-09
Container
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Open access
unknown

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BibTeX

@article{allodium:10.1016/j.artmed.2025.103138,
  title = {An AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels},
  author = {Niamh Belton and Aonghus Lawlor and Kathleen M. Curran},
  year = {2025},
  journal = {Artificial Intelligence in Medicine},
  doi = {10.1016/j.artmed.2025.103138},
  url = {https://doi.org/10.1016/j.artmed.2025.103138}
}

RIS

TY  - JOUR
TI  - An AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels
AU  - Niamh Belton
AU  - Aonghus Lawlor
AU  - Kathleen M. Curran
PY  - 2025
JO  - Artificial Intelligence in Medicine
DO  - 10.1016/j.artmed.2025.103138
UR  - https://doi.org/10.1016/j.artmed.2025.103138
ER  - 

APA

Belton, N., Lawlor, A., & Curran, K. M. (2025). An AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2025.103138

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