Fully vs. Weakly Supervised Caries Localization in Smartphone Images with CNNs

While in developed countries routine dental consultations are often covered by insurance, access to prophylactic dental examinations is often expensive in developing countries. Therefore, sufficient oral health prevention, particularly early caries detection, is not accessible to many people in these countries, yet. This observation is, however, contrary to the accessibility of smartphone technology, as smartphones have become available and affordable in most countries. Their technology can be utilized for low-cost initial caries inspection to determine the necessity for a subsequent dental examination. In this paper we address the specific problem of caries detection in smartphone images. Fully supervised methods usually require tedious location annotations, whereas weakly supervised approaches manage to address the detection task with less complex labels. To this end, we propose a weakly supervised caries detection strategy with local constraints and investigate its caries localization capabilities compared to a superior fully supervised Faster R-CNN approach as upper baseline. Our proposed strategy shows promising initial results on our in-house smartphone caries data set.