Top

Published in:

2020 | OriginalPaper | Chapter

Paying Per-Label Attention for Multi-label Extraction from Radiology Reports

Authors : Patrick Schrempf, Hannah Watson, Shadia Mikhael, Maciej Pajak, Matúš Falis, Aneta Lisowska, Keith W. Muir, David Harris-Birtill, Alison Q. O’Neil

Published in: Interpretable and Annotation-Efficient Learning for Medical Image Computing

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly, we propose a set of 31 labels which correspond to radiographic findings (e.g. hyperdensity) and clinical impressions (e.g. haemorrhage) related to neurological abnormalities. Secondly, inspired by previous work, we extend existing state-of-the-art neural network models with a label-dependent attention mechanism. Using this mechanism and simple synthetic data augmentation, we are able to robustly extract many labels with a single model, classified according to the radiologist’s reporting (positive, uncertain, negative). This approach can be used in further research to effectively extract many labels from medical text.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset

Available only for authorised users

iCAIRD project number: 104690; University of St Andrews: CS14871.

https://github.com/google-research/bert.

https://github.com/th0mi/clinicalBERT.

Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72–78. Association for Computational Linguistics, Minneapolis, Minnesota, USA, Jun 2019. https://doi.org/10.18653/v1/W19-1909

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)

Banerjee, S., Akkaya, C., Perez-Sorrosal, F., Tsioutsiouliklis, K.: Hierarchical transfer learning for multi-label text classification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6295–6300 (2019)

Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(90001), 267D–270 (2004). https://doi.org/10.1093/nar/gkh061CrossRef

Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111. Association for Computational Linguistics, Doha, Qatar, October 2014.https://doi.org/10.3115/v1/W14-4012

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https://doi.org/10.18653/v1/N19-1423

Drozdov, I., et al.: Supervised and unsupervised language modelling in chest x-ray radiological reports. Plos One 15(3), e0229963 (2020)CrossRef

Gorinski, P.J., et al.: Named entity recognition for electronic health records: a comparison of rule-based and machine learning approaches. arXiv preprint arXiv:1903.03985 (2019)

Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 590–597 (2019)

10.

IST-3 collaborative group: Association between brain imaging signs, early and late outcomes, and response to intravenous alteplase after acute ischaemic stroke in the third International Stroke Trial (IST-3): secondary analysis of a randomised controlled trial. Lancet Neurol. 14, pp. 485–496 (2015). https://doi.org/10.1016/S1474-4422(15)00012-5

11.

Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)CrossRef

12.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)

13.

Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Association for Computational Linguistics, Philadelphia (2002)

14.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

15.

Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., Eisenstein, J.: Explainable prediction of medical codes from clinical text. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1101–1111. Association for Computational Linguistics, New Orleans, Louisiana, Jun 2018. https://doi.org/10.18653/v1/N18-1100

16.

Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH

17.

Radiological Society of North America: RSNA Intracranial Hemorrhage Detection (Kaggle challenge). https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/overview

18.

Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, Valletta, Malta, pp. 45–50, May 2010

19.

Smit, A., Jain, S., Rajpurkar, P., Pareek, A., Ng, A.Y., Lungren, M.P.: CheXbert: combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. arXiv preprint arXiv:2004.09167 (2020)

20.

Wolf, T., et al.: HuggingFace’s transformers: state-of-the-art natural language processing. ArXiv abs/1910.03771 (2019)

21.

Wood, D., et al.: Automated labelling using an attention model for radiology reports of MRI scans (ALARM). In: Medical Imaging with Deep Learning (2020). https://openreview.net/forum?id=UFnWZTbM5t

22.

Yadav, K., Sarioglu, E., Choi, H., Cartwright IV, W.B., Hinds, P.S., Chamberlain, J.M.: Automated outcome classification of computed tomography imaging reports for pediatric traumatic brain injury. Acad. Emerg. Med. 23(2), 171–178 (2016). https://doi.org/10.1111/acem.12859CrossRef

23.

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)

24.

Yetisgen-Yildiz, M., Gunn, M.L., Xia, F., Payne, T.H.: A text processing pipeline to extract recommendations from radiology reports. J. Biomed. Inf. 46(2), 354–362 (2013)CrossRef

25.

Zech, J., et al.: Natural language-based machine learning models for the annotation of clinical radiology reports. Radiology 287(2), 570–580 (2018)MathSciNetCrossRef

Title: Paying Per-Label Attention for Multi-label Extraction from Radiology Reports
Authors: Patrick Schrempf
Hannah Watson
Shadia Mikhael
Maciej Pajak
Matúš Falis
Aneta Lisowska
Keith W. Muir
David Harris-Birtill
Alison Q. O’Neil
Publisher: Springer International Publishing
Book: Interpretable and Annotation-Efficient Learning for Medical Image Computing
Print ISBN: 978-3-030-61165-1

Electronic ISBN: 978-3-030-61166-8

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-61166-8_29

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"