Abstract
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.
This material is based upon work supported by Google Cloud Research credit program.
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Monshi, M.M.A., Poon, J., Chung, V., Monshi, F.M. (2021). Labeling Chest X-Ray Reports Using Deep Learning. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_55
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