Accurate classification of periodontal disease through panoramic X-ray images carries immense clinical importance for effective diagnosis and treatment. Recent methodologies attempt to classify periodontal diseases from X-ray images by estimating bone loss within these images, supervised by manual radiographic annotations for segmentation or keypoint detection. However, these annotations often lack consistency with the clinical gold standard of probing measurements, potentially causing measurement inaccuracy and leading to unstable classifications. Additionally, the diagnosis of periodontal disease necessitates exceptional sensitivity. To address these challenges, we introduce HC-Net, an innovative hybrid classification framework devised for accurately classifying periodontal disease from X-ray images. This framework comprises three main components: tooth-level classification, patient-level classification, and a learnable adaptive noisy-OR gate. In the tooth-level classification, we initially employ instance segmentation to individually identify each tooth, followed by tooth-level periodontal disease classification. For patient-level classification, we utilize a multi-task strategy to concurrently learn patient-level classification and a Classification Activation Map (CAM) that signifies the confidence of local lesion areas within the panoramic X-ray image. Eventually, our adaptive noisy-OR gate acquires a hybrid classification by amalgamating predictions from both levels. In particular, we incorporate clinical knowledge into the workflows used by professional dentists, targeting the enhanced handling of sensitivity of periodontal disease diagnosis. Extensive empirical testing on a dataset amassed from real-world clinics demonstrates that our proposed HC-Net achieves unparalleled performance in periodontal disease classification, exhibiting substantial potential for practical application.

中文翻译：

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临床知识引导的混合分类网络，用于 X 线图像中牙周病的自动诊断


通过全景 X 射线图像对牙周病进行准确分类，对于有效诊断和治疗具有极大的临床意义。最近的方法试图通过估计 X 射线图像中的骨质流失来从 X 射线图像中对牙周病进行分类，并由手动射线照相注释监督以进行分割或关键点检测。然而，这些注释通常与探测测量的临床金标准缺乏一致性，可能导致测量不准确并导致分类不稳定。此外，牙周病的诊断需要特别的敏感性。为了应对这些挑战，我们引入了 HC-Net，这是一种创新的混合分类框架，旨在从 X 射线图像中准确分类牙周病。该框架包括三个主要组成部分：牙齿级别分类、患者级别分类和可学习的自适应噪声手术室门。在牙齿级别分类中，我们最初采用实例分割来单独识别每颗牙齿，然后是牙齿级别的牙周病分类。对于患者级别的分类，我们利用多任务策略来同时学习患者级别的分类和分类激活图 （CAM），该地图表示全景 X 射线图像中局部病变区域的置信度。最终，我们的自适应噪声 OR 门通过合并两个级别的预测来获得混合分类。特别是，我们将临床知识融入专业牙医使用的工作流程中，旨在加强对牙周病诊断敏感性的处理。 对从真实世界诊所收集的数据进行的广泛实证测试表明，我们提出的 HC-Net 在牙周病分类方面取得了无与伦比的性能，显示出巨大的实际应用潜力。