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dc.contributor.authorSchrempf, Patrick
dc.contributor.authorWatson, Hannah
dc.contributor.authorMikhael, Shadia
dc.contributor.authorPajak, Maciej
dc.contributor.authorFalis, Matúš
dc.contributor.authorLisowska, Aneta
dc.contributor.authorMuir, Keith W.
dc.contributor.authorHarris-Birtill, David
dc.contributor.authorO'Neil, Alison Q.
dc.contributor.editorCardoso, Jaime
dc.contributor.editorVan Nguyen, Hien
dc.contributor.editorHeller, Nicholas
dc.contributor.editorHenriques Abreu, Pedro
dc.contributor.editorIsgum, Ivana
dc.contributor.editorSilva, Wilson
dc.contributor.editorCruz, Ricardo
dc.contributor.editorPereira Amorim, Jose
dc.contributor.editorPatel, Vishal
dc.contributor.editorRoysam, Badri
dc.contributor.editorZhou, Kevin
dc.contributor.editorJiang, Steve
dc.contributor.editorLe, Ngan
dc.contributor.editorLuu, Khoa
dc.contributor.editorSznitman, Raphael
dc.contributor.editorCheplygina, Veronika
dc.contributor.editorMateus, Diana
dc.contributor.editorTrucco, Emanuele
dc.contributor.editorAbbasi, Samaneh
dc.identifier.citationSchrempf , P , Watson , H , Mikhael , S , Pajak , M , Falis , M , Lisowska , A , Muir , K W , Harris-Birtill , D & O'Neil , A Q 2020 , Paying per-label attention for multi-label extraction from radiology reports . in J Cardoso , H Van Nguyen , N Heller , P Henriques Abreu , I Isgum , W Silva , R Cruz , J Pereira Amorim , V Patel , B Roysam , K Zhou , S Jiang , N Le , K Luu , R Sznitman , V Cheplygina , D Mateus , E Trucco & S Abbasi (eds) , Interpretable and Annotation-Efficient Learning for Medical Image Computing : Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings . Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics) , vol. 12446 LNCS , Springer , Cham , pp. 277-289 , MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis 2020 , Peru , 8/10/20 .
dc.identifier.otherPURE: 270621167
dc.identifier.otherPURE UUID: b39b5af5-ee08-4d4a-aa40-7937a4e10b36
dc.identifier.otherBibtex: 10.1007/978-3-030-61166-8_29
dc.identifier.otherORCID: /0000-0003-2484-6855/work/81797912
dc.identifier.otherORCID: /0000-0002-0740-3668/work/81798001
dc.identifier.otherScopus: 85092889547
dc.descriptionFunding: This work is part of the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690].en
dc.description.abstractTraining 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.
dc.relation.ispartofInterpretable and Annotation-Efficient Learning for Medical Image Computingen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics)en
dc.rightsCopyright © 2020 Springer Nature Switzerland AG. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectRadiology report labellingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC0321 Neuroscience. Biological psychiatry. Neuropsychiatryen
dc.titlePaying per-label attention for multi-label extraction from radiology reportsen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen

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