Diagnosing dental diseases from radiographs is time-consuming and challenging due to the subtle nature of diagnostic evidence. Existing methods, which rely on object detection models designed for natural images with more distinct target patterns, struggle to detect dental diseases that present with far less visual support. To address this challenge, we propose {\bf DentalX}, a novel context-aware dental disease detection approach that leverages oral structure information to mitigate the visual ambiguity inherent in radiographs. Specifically, we introduce a structural context extraction module that learns an auxiliary task: semantic segmentation of dental anatomy. The module extracts meaningful structural context and integrates it into the primary disease detection task to enhance the detection of subtle dental diseases. Extensive experiments on a dedicated benchmark demonstrate that DentalX significantly outperforms prior methods in both tasks. This mutual benefit arises naturally during model optimization, as the correlation between the two tasks is effectively captured. Our code is available at https://github.com/zhiqin1998/DentYOLOX.

翻译：从放射影像诊断牙科疾病因诊断证据的细微性而耗时且具有挑战性。现有方法依赖于为具有更明显目标模式的自然图像设计的物体检测模型，难以检测视觉支持远为不足的牙科疾病。为应对这一挑战，我们提出{\bf DentalX}，一种新颖的上下文感知牙科疾病检测方法，该方法利用口腔结构信息来缓解放射影像固有的视觉模糊性。具体而言，我们引入了一个结构上下文提取模块，该模块学习一个辅助任务：牙齿解剖结构的语义分割。该模块提取有意义的结构上下文并将其整合到主要疾病检测任务中，以增强对细微牙科疾病的检测。在专用基准测试上进行的大量实验表明，DentalX在两项任务上均显著优于先前方法。这种相互增益在模型优化过程中自然产生，因为两个任务之间的相关性被有效捕获。我们的代码可在 https://github.com/zhiqin1998/DentYOLOX 获取。