Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study-Reference-Cited by-同舟云学术

Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study

Published:2020 Issue: Volume: Page:254-265
ISSN:0302-9743
Container-title:Interpretable and Annotation-Efficient Learning for Medical Image Computing
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Author:

Wood David A.,Kafiabadi Sina,Al Busaidi Aisha,Guilhem Emily,Lynch Jeremy,Townend Matthew,Montvila Antanas,Siddiqui Juveria,Gadapa Naveen,Benger Matthew,Barker Gareth,Ourselin Sebastian,Cole James H.,Booth Thomas C.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-61166-8_27

Reference14 articles.

1. Berbaum, K., Franken, E., Caldwell, R., Schartz, K., Madsen, M.: Satisfaction of Search in Radiology, 2nd edn., pp. 121–166. Cambridge University Press, Cambridge (2018). https://doi.org/10.1017/9781108163781.010

2. Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders (2018). arXiv:1806.04972

3. Chiewvit, P., Piyapittayanan, S., Poungvarin, N.: Cerebral venous thrombosis: diagnosis dilemma. Neurol. Int. 3, e13 (2011). https://doi.org/10.4081/ni.2011.e13

4. Cocos, A., Masino, A., Qian, T., Pavlick, E., Callison-Burch, C.: Effectively crowdsourcing radiology report annotations. In: Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis, pp. 109–114. Association for Computational Linguistics, Lisbon, Portugal, September 2015. https://doi.org/10.18653/v1/W15-2614, https://www.aclweb.org/anthology/W15-2614

5. Crump, M.J.C., McDonnell, J.V., Gureckis, T.M.: Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PLOS One 8(3), 1–18 (03 2013). https://doi.org/10.1371/journal.pone.0057410

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