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2020 | OriginalPaper | Buchkapitel

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

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

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

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset

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

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

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

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