首个全景牙片病灶检测数据集，助力牙周炎智能诊断
PerioDet: Large-Scale Panoramic Radiograph Benchmark for Clinical-Oriented Apical Periodontitis Detection
- 提出结合去噪注意力与动态框校准的检测框架
- 在3673张牙片上实现5662个病灶精准定位
- 适合口腔AI研发及临床辅助诊断系统开发
根尖周炎是常见口腔疾病，严重威胁公共健康。尽管医学影像智能诊断取得进展，但其在根尖周炎领域的应用仍受限于缺乏大规模高质量标注数据。为此，我们发布了首个用于自动化根尖周炎诊断的大规模全景牙片基准数据集PerioXrays，包含3,673张图像和5,662个精细标注的病灶实例。本文进一步提出面向临床的根尖周炎检测方法PerioDet，融合背景去噪注意力（BDA）与IoU动态校准（IDC）机制，有效应对背景干扰与小目标检测难题。在PerioXrays上的大量实验表明，PerioDet显著提升检测性能。此外，人机协作实验验证了该方法作为专业牙医辅助诊断工具的临床实用性。
原文摘要 · Abstract (English)
Apical periodontitis is a prevalent oral pathology that presents significant public health challenges. Despite advances in automated diagnostic systems across various medical fields, the development of Computer-Aided Diagnosis (CAD) applications for apical periodontitis is still constrained by the lack of a large-scale, high-quality annotated dataset. To address this issue, we release a large-scale panoramic radiograph benchmark called "PerioXrays", comprising 3,673 images and 5,662 meticulously annotated instances of apical periodontitis. To the best of our knowledge, this is the first benchmark dataset for automated apical periodontitis diagnosis. This paper further proposes a clinical-oriented apical periodontitis detection (PerioDet) paradigm, which jointly incorporates Background-Denoising Attention (BDA) and IoU-Dynamic Calibration (IDC) mechanisms to address the challenges posed by background noise and small targets in automated detection. Extensive experiments on the PerioXrays dataset demonstrate the superiority of PerioDet in advancing automated apical periodontitis detection. Additionally, a well-designed human-computer collaborative experiment underscores the clinical applicability of our method as an auxiliary diagnostic tool for professional dentists.
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