2023
DOI: 10.1007/s11548-023-02965-4
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Automated screening of computed tomography using weakly supervised anomaly detection

Abstract: Background 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 an… Show more

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