Rad-SpRL labels findings, locations, diagnoses, and hedges in chest X-ray reports, and a Bi-LSTM-CRF model reaches average F1 of 90.28, 94.61, 71.47, and 73.27 on those four roles.
Corry, The future of recruitment and selection in radiology
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Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning
Rad-SpRL labels findings, locations, diagnoses, and hedges in chest X-ray reports, and a Bi-LSTM-CRF model reaches average F1 of 90.28, 94.61, 71.47, and 73.27 on those four roles.