Abstract
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 this https URL.
Abstract (translated)
从放射影像诊断牙科疾病是一项耗时且具有挑战性的任务，因为诊断证据往往非常细微。现有的方法依赖于为自然图像设计的对象检测模型，这些模型在目标模式更为明显的场景下表现良好，但在面对牙科疾病这样视觉支持较少的情况下则难以有效检测出病症。为了应对这一挑战，我们提出了{\bf DentalX}，这是一种新型的基于上下文感知的牙科疾病检测方法，利用口腔结构信息来减轻放射影像中固有的视觉模糊性。 具体而言，DentalX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：牙科解剖学的语义分割。这个模块可以提取有意义的结构上下文，并将其整合到主要的疾病检测任务中，从而增强对细微牙科疾病的检测能力。在专门为此设计的数据集上进行的大量实验表明，DentalX在这两项任务上的表现都显著优于以前的方法。这种互相增益的效果自然地出现在模型优化过程中，因为该方法能够有效捕捉两个任务之间的相关性。 我们的代码可在此网址获取：[此链接]（请提供实际的有效链接）。
URL
https://arxiv.org/abs/2601.08797
