Recurrent Neural Network (RNN) models have been widely used for sequence labeling applications in different domains. This paper presents an RNN-based sequence labeling approach with the ability to learn long-term labeling dependencies. The proposed model has been successfully used for a Named Entity Recognition challenge in healthcare: anatomical phrase labeling in radiology reports. The model was trained on labeled data from a radiology report corpus and tested on two independent datasets. The proposed model achieved promising performance in comparison with other state-of-the-art context-driven sequence labeling approaches.

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540J (15 March 2019); doi: 10.1117/12.2512103

Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)