When CNNs Outperform Transformers and Mambas: Revisiting Deep Architectures for Dental Caries Segmentation

TL;DR

This study benchmarks various deep learning architectures for dental caries segmentation in radiographs, revealing CNNs outperform transformers and Mambas in accuracy despite the trend towards complex models.

Contribution

It provides the first comprehensive comparison of CNNs, transformers, and Mamba architectures for dental caries segmentation on a new dataset, highlighting CNNs' superior performance.

Findings

CNN-based DoubleU-Net achieved the highest metrics.

Transformers and Mambas underperformed due to limited data.

Architecture-task alignment is more crucial than model complexity.

Abstract

Accurate identification and segmentation of dental caries in panoramic radiographs are critical for early diagnosis and effective treatment planning. Automated segmentation remains challenging due to low lesion contrast, morphological variability, and limited annotated data. In this study, we present the first comprehensive benchmarking of convolutional neural networks, vision transformers and state-space mamba architectures for automated dental caries segmentation on panoramic radiographs through a DC1000 dataset. Twelve state-of-the-art architectures, including VMUnet, MambaUNet, VMUNetv2, RMAMamba-S, TransNetR, PVTFormer, DoubleU-Net, and ResUNet++, were trained under identical configurations. Results reveal that, contrary to the growing trend toward complex attention based architectures, the CNN-based DoubleU-Net achieved the highest dice coefficient of 0.7345, mIoU of 0.5978, and precision of 0.8145, outperforming all transformer and Mamba variants. In the study, the top 3 results across all performance metrics were achieved by CNN-based architectures. Here, Mamba and transformer-based methods, despite their theoretical advantage in global context modeling, underperformed due to limited data and weaker spatial priors. These findings underscore the importance of architecture-task alignment in domain-specific medical image segmentation more than model complexity. Our code is available at: https://github.com/JunZengz/dental-caries-segmentation.