  Deep learning-based RGB caries detection improves the efficiency of caries
identification and is crucial for preventing oral diseases. The performance of
deep learning models depends on high-quality data and requires substantial
training resources, making efficient deployment challenging. Core data
selection, by eliminating low-quality and confusing data, aims to enhance
training efficiency without significantly compromising model performance.
However, distance-based data selection methods struggle to distinguish
dependencies among high-dimensional caries data. To address this issue, we
propose a Core Data Selection Method with Jensen-Shannon Divergence (JSCDS) for
efficient caries image learning and caries classification. We describe the core
data selection criterion as the distribution of samples in different classes.
JSCDS calculates the cluster centers by sample embedding representation in the
caries classification network and utilizes Jensen-Shannon Divergence to compute
the mutual information between data samples and cluster centers, capturing
nonlinear dependencies among high-dimensional data. The average mutual
information is calculated to fit the above distribution, serving as the
criterion for constructing the core set for model training. Extensive
experiments on RGB caries datasets show that JSCDS outperforms other data
selection methods in prediction performance and time consumption. Notably,
JSCDS exceeds the performance of the full dataset model with only 50% of the
core data, with its performance advantage becoming more pronounced in the 70%
of core data.
