Application of Adversarial Training in the Detection of Calcification Regions from Dental Panoramic Radiographs

Abstract

Calcification regions, which may be observed on dental panoramic radiographs, are a sign of vascular disease. Therefore, automatic detection methods based on semantic segmentation (SS) have been proposed. However, because of the small amount of data in the available dataset, the segmentation accuracy was insufficient. This paper proposes a method that uses adversarial features (AFs) for this problem. We extend AFs, which are an adversarial training method for discriminative problems, to SS. The proposed method can improve performance, even with a small amount of data.