Automatic intracranial abnormality detection and localization in head CT scans by learning from free-text reports.
Cell Rep Med
; 4(9): 101164, 2023 09 19.
Article
en En
| MEDLINE
| ID: mdl-37652014
ABSTRACT
Deep learning has yielded promising results for medical image diagnosis but relies heavily on manual image annotations, which are expensive to acquire. We present Cross-DL, a cross-modality learning framework for intracranial abnormality detection and localization in head computed tomography (CT) scans by learning from free-text imaging reports. Cross-DL has a discretizer that automatically extracts discrete labels of abnormality types and locations from reports, which are utilized to train an image analyzer by a dynamic multi-instance learning approach. Benefiting from the low annotation cost and a consequent large-scale training set of 28,472 CT scans, Cross-DL achieves accurate performance, with an average area under the receiver operating characteristic curve (AUROC) of 0.956 (95% confidence interval 0.952-0.959) in detecting 4 abnormality types in 17 regions while accurately localizing abnormalities at the voxel level. An intracranial hemorrhage classification experiment on the external dataset CQ500 achieves an AUROC of 0.928 (0.905-0.951). The model can also help review prioritization.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Tomografía Computarizada por Rayos X
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
Cell Rep Med
Año:
2023
Tipo del documento:
Article
País de afiliación:
China