Journal
CELL REPORTS MEDICINE
Volume 4, Issue 9, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.xcrm.2023.101164
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This study presents a cross-modality learning framework called Cross-DL for detecting and localizing intracranial abnormalities in head CT scans by learning from free-text imaging reports. The framework includes a discretizer that automatically extracts abnormality types and locations from reports and trains an image analyzer using dynamic multi-instance learning. With a large-scale training set, Cross-DL achieves accurate performance in detecting and localizing abnormalities.
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.
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