Login / Signup

Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection.

Haruyuki WatanabeYuina EzawaEri MatsuyamaYohan KondoNorio HayashiSho MaruyamaToshihiro OguraMasayuki Shimosegawa
Published in: Journal of X-ray science and technology (2024)
Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.
Keyphrases