Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: A multi-center retrospective study
Junya Sato,
Kento Sugimoto,
Yuki Suzuki
et al.
Abstract:SUMMARYBackgroundArtificial intelligence (AI) systems designed to detect abnormalities in abdominal computed tomography (CT) could reduce radiologists’ workload and improve diagnostic processes. However, development of such models has been hampered by the shortage of large expert-annotated datasets. Here, we used information from free-text radiology reports, rather than manual annotations, to develop a deep-learning-based pipeline for comprehensive detection of abdominal CT abnormalities.MethodsIn this multice… Show more
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