Accurate detection of ultrasound nodules is essential for the early diagnosis and treatment of thyroid and breast cancers. However, this task remains challenging due to irregular nodule shapes, indistinct boundaries, substantial scale variations, and the presence of speckle noise that degrades structural visibility. To address these challenges, we propose a prior-guided DETR framework specifically designed for ultrasound nodule detection. Instead of relying on purely data-driven feature learning, the proposed framework progressively incorporates different prior knowledge at multiple stages of the network. First, a Spatially-adaptive Deformable FFN with Prior Regularization (SDFPR) is embedded into the CNN backbone to inject geometric priors into deformable sampling, stabilizing feature extraction for irregular and blurred nodules. Second, a Multi-scale Spatial-Frequency Feature Mixer (MSFFM) is designed to extract multi-scale structural priors, where spatial-domain processing emphasizes contour continuity and boundary cues, while frequency-domain modeling captures global morphology and suppresses speckle noise. Furthermore, a Dense Feature Interaction (DFI) mechanism propagates and exploits these prior-modulated features across all encoder layers, enabling the decoder to enhance query refinement under consistent geometric and structural guidance. Experiments conducted on two clinically collected thyroid ultrasound datasets (Thyroid I and Thyroid II) and two public benchmarks (TN3K and BUSI) for thyroid and breast nodules demonstrate that the proposed method achieves superior accuracy compared with 18 detection methods, particularly in detecting morphologically complex nodules.The source code is publicly available at https://github.com/wjj1wjj/Ultrasound-DETR.

翻译：超声结节的精确检测对于甲状腺癌和乳腺癌的早期诊断与治疗至关重要。然而，由于结节形状不规则、边界模糊、尺度变化显著以及散斑噪声降低结构可见性，该任务仍具挑战性。为应对这些挑战，本文提出一种专为超声结节检测设计的先验引导DETR框架。该框架摒弃纯数据驱动的特征学习方式，在网络多个阶段渐进式融入不同先验知识。首先，在CNN主干网络中嵌入具有先验正则化的空间自适应可变形前馈网络，将几何先验注入可变形采样过程，从而稳定不规则与模糊结节的特征提取。其次，设计多尺度空频特征混合器以提取多尺度结构先验：空域处理强调轮廓连续性与边界线索，频域建模则捕获全局形态并抑制散斑噪声。此外，通过密集特征交互机制在先验调制特征在编码器各层间传播与利用，使解码器能在一致的几何与结构引导下增强查询优化。在两个临床采集的甲状腺超声数据集（Thyroid I与Thyroid II）及两个甲状腺与乳腺结节的公开基准数据集（TN3K与BUSI）上的实验表明，所提方法相较于18种检测方法实现了更优的检测精度，尤其在形态复杂结节的检测中表现突出。源代码已公开于https://github.com/wjj1wjj/Ultrasound-DETR。