Deep learning to automate the labelling of head MRI datasets for computer vision applications.

ABSTRACT:

Objectives

The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development.

Methods

Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, m

SUBMITTER: Wood DA 

PROVIDER: S-EPMC8660736 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature