HC-Net: Hybrid Classification Network for Automatic Periodontal Disease Diagnosis

Abstract

Accurate periodontal disease classification from panoramic X-ray images is of great significance for efficient clinical diagnosis and treatment. It has been a challenging task due to the subtle evidence in radiography. Recent methods attempt to estimate bone loss on these images to classify periodontal diseases, relying on the radiographic manual annotations to supervise segmentation or keypoint detection. However, these radiographic annotations are inconsistent with the clinical golden standard of probing measurements and thus can lead to measurement errors and unstable classifications. In this paper, we propose a novel hybrid classification framework, HC-Net, for accurate periodontal disease classification from X-ray images, which consists of three components, i.e., tooth-level classification, patient-level classification, and a learnable adaptive noisy-OR gate. Specifically, in the tooth-level classification, we first introduce instance segmentation to capture each tooth, and then classify the periodontal disease in the tooth level. As for the patient level, we exploit a multi-task strategy to jointly learn patient-level classification and classification activation map (CAM) that reflects the confidence of local lesion areas upon the panoramic X-ray image. Eventually, the adaptive noisy-OR gate obtains a hybrid classification by integrating predictions from both levels. Extensive experiments on the dataset collected from real-world clinics demonstrate that our proposed HC-Net achieves state-of-the-art performance in periodontal disease classification and shows great application potential. Our code is available at https://github.com/ShanghaiTech-IMPACT/Periodental_Disease.