Paying per-label attention for multi-label extraction from radiology reports
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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.
Schrempf , 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 . https://doi.org/10.1007/978-3-030-61166-8_29workshop
Interpretable and Annotation-Efficient Learning for Medical Image Computing
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