Automated screening of computed tomography using weakly supervised anomaly detection

Atsuhiro Hibi, Michael D. Cusimano, Alexander Bilbily, Rahul G. Krishnan, Pascal N. Tyrrell

Abstract: Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload.