One of the primary challenges in the development of Chest X-Ray (CXR) interpretation models has been the lack of large datasets with multilabel image annotations extracted from radiology reports. This paper proposes a CXR labeler that can simultaneously extracts fourteen observations from free-text radiology reports as positive or negative, abbreviated as CXRlabeler. It fine-tunes a pre-trained language model, AWD-LSTM, to the corpus of CXR radiology impressions and then uses it as the base of the multilabel classifier. Experimentation demonstrates that a language model fine-tuning increases the classifier F1 score by 12.53%. Overall, CXRlabeler achieves a 96.17% F1 score on the MIMIC-CXR dataset. To further test the generalization of the CXRlabeler model, it is tested on the PadChest dataset. This testing shows that the CXRlabeler approach is helpful in a different language environment, and the model (available at https://github.com/MaramMonshi/CXRlabeler ) can assist researchers in labeling CXR datasets with fourteen observations.
CITATION STYLE
Monshi, M. M. A., Poon, J., Chung, V., & Monshi, F. M. (2021). Labeling Chest X-Ray Reports Using Deep Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12893 LNCS, pp. 684–694). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86365-4_55
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