An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars

• URL: http://arxiv.org/abs/2509.09911v1
• Date: Fri, 12 Sep 2025 00:54:07 GMT
• Title: An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars
• Authors: Barkin Buyukcakir, Jannick De Tobel, Patrick Thevissen, Dirk Vandermeulen, Peter Claes,
• Abstract summary: This study introduces a framework designed to enhance both performance and transparency in high-stakes forensic applications.<n>We use a notable performance disparity in the automated staging of mandibular second (tooth 37) and third (tooth 38) molars as a case study.
• Score: 4.6984251688936425
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The practical adoption of deep learning in high-stakes forensic applications, such as dental age estimation, is often limited by the 'black box' nature of the models. This study introduces a framework designed to enhance both performance and transparency in this context. We use a notable performance disparity in the automated staging of mandibular second (tooth 37) and third (tooth 38) molars as a case study. The proposed framework, which combines a convolutional autoencoder (AE) with a Vision Transformer (ViT), improves classification accuracy for both teeth over a baseline ViT, increasing from 0.712 to 0.815 for tooth 37 and from 0.462 to 0.543 for tooth 38. Beyond improving performance, the framework provides multi-faceted diagnostic insights. Analysis of the AE's latent space metrics and image reconstructions indicates that the remaining performance gap is data-centric, suggesting high intra-class morphological variability in the tooth 38 dataset is a primary limiting factor. This work highlights the insufficiency of relying on a single mode of interpretability, such as attention maps, which can appear anatomically plausible yet fail to identify underlying data issues. By offering a methodology that both enhances accuracy and provides evidence for why a model may be uncertain, this framework serves as a more robust tool to support expert decision-making in forensic age estimation.

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