Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT

Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT

Accurate segmentation of radiolucent lesions in dental Cone-Beam Computed Tomography (CBCT) is vital for enhancing diagnostic reliability and reducing the burden on clinicians. This study proposes a privacy-preserving segmentation framework leveraging multiple U-Net variants—U-Net, DoubleU-Net, U2-N...

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Bibliographic Details
Main Authors: Amelia Ritahani Ismail, Faris Farhan Azlan, Khairul Akmal Noormaizan, Nurul Afiqa, Syed Qamrun Nisa, Ahmad Badaruddin Ghazali, Andri Pranolo, Shoffan Saifullah
Format: Article
Language:English
Published: Universitas Ahmad Dahlan 2025-05-01
Series:IJAIN (International Journal of Advances in Intelligent Informatics)
Online Access:https://ijain.org/index.php/IJAIN/article/view/1529
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https://ijain.org/index.php/IJAIN/article/view/1529

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