CaliDent-Net: domain-constrained self-supervised pre-training with parallel attention and prototype calibration for dental radiograph analysis

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Automated interpretation of dental radiographs is limited by the shortage of expert-annotated training images and by standard softmax classifiers that often assign confidence values that do not reflect empirical accuracy. We introduce CaliDent-Net , a unified framework that integrates and domain-adapts three established techniques contrastive self-supervision, dual attention, and prototype-based classification to obtain four clinically relevant properties in a single pipeline: data efficiency, probabilistic calibration, case-based interpretability, and CPU-level inference. CaliDent-Net consists of a Radiograph-Aware Contrastive Encoder (RACE), which performs pre-training with augmentations that respect tooth and bone anatomy; a Parallel Recalibration Attention Block (PRAB), which computes spatial saliency and channel importance through two forward-independent pathways before learned fusion; and a Similarity-Scored Prototype Classifier (SSPC), which replaces the unconstrained linear head with bounded cosine-prototype scoring. SSPC mitigates logit inflation, supports nearest-prototype retrieval as a case-based explanation mechanism, and improves calibration at the architectural level rather than relying only on post-hoc correction. We evaluate CaliDent-Net on the public Dental Radiography benchmark (1,272 images, four pathological classes) against eight deep learning baselines under matched protocols. The proposed model achieves 96.4% accuracy and a macro-averaged AUC of 0.984, representing an improvement of 2.3 to 9.5 percentage points over the baselines. Its Brier score (0.021) and expected calibration error (0.013) are 45% and 71% lower than the ResNet-50 reference (0.038 and 0.046), with only a minor portion of the calibration gain attributable to temperature scaling. With 40% of the labels (about 356 images), CaliDent-Net exceeds the full-data ResNet-50 baseline, and its CPU inference time is 214 ms per image at 2.8 GFLOPs. The findings support an integrative recipe rather than a new learning primitive, but they do not establish clinical translation; multi-site prospective validation, blinded multi-rater interpretability evaluation, and domain-shift robustness assessment remain necessary.