Uncertainty-aware automatic TNM staging classification for [ ¹⁸ F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.

Autor: Barlow SH; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. stephen.barlow@kcl.ac.uk., Chicklore S; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.; King's College London and Guy's and St. Thomas' PET Centre, St. Thomas' Hospital, London, UK., He Y; Department of Informatics, King's College London, London, UK.; Department of Computer Science, University of Warwick, Coventry, UK.; Alan Turing Institute, London, UK., Ourselin S; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK., Wagner T; Department of Nuclear Medicine, Royal Free Hospital, London, UK., Barnes A; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.; King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, London, UK., Cook GJR; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.; King's College London and Guy's and St. Thomas' PET Centre, St. Thomas' Hospital, London, UK.

Publikováno v: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Dec 18; Vol. 24 (1), pp. 396. Date of Electronic Publication: 2024 Dec 18.