Automatic intracranial abnormality detection and localization in head CT scans by learning from free-text reports

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.

Automated summary

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.