Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning

Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning

Abstract Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based approaches often suffer from high comput...

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Bibliographic Details
Main Authors: Najmeh Pishghadam, Rasool Esmaeilyfard, Maryam Paknahad
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
CBCT
Multi-task learning
Attention mechanisms
Age Estimation
Gender classification
Explainable AI (XAI)
Online Access:https://doi.org/10.1038/s41598-025-03305-z
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https://doi.org/10.1038/s41598-025-03305-z

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