利用牙齿解剖结构信息提升口腔X光片中细微病变的检测能力
DentalX: Context-Aware Dental Disease Detection with Radiographs
- 通过解析牙齿解剖结构作为上下文辅助诊断
- 在专用数据集上显著优于现有方法，提升细微病变检出率
- 适合牙科医学图像分析与医疗AI研究者参考
从口腔X光片中诊断牙病耗时且困难，因诊断依据往往不明显。现有基于自然图像设计的目标检测模型难以应对牙病视觉线索微弱的问题。为此，我们提出DentalX，一种新颖的上下文感知牙病检测方法，利用口腔解剖结构信息缓解影像中的视觉模糊。具体而言，引入结构上下文提取模块，学习牙齿解剖的语义分割这一辅助任务，提取有意义的结构上下文，并融入主任务以增强对细微牙病的检测。在专用基准上的大量实验表明，DentalX在两项任务中均显著优于先前方法。这种互益效果在模型优化过程中自然产生，因两任务间相关性被有效捕捉。代码已公开于https://github.com/zhiqin1998/DentYOLOX。
原文摘要 · Abstract (English)
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.
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