Abstract
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 this http URL source code is publicly available at this https URL.
Abstract (translated)
准确检测超声结节对于甲状腺和乳腺癌的早期诊断和治疗至关重要。然而，由于结节形状不规则、边界模糊不清、尺度变化大以及斑点噪声导致结构可见性降低等原因，这项任务仍然极具挑战性。为了应对这些挑战，我们提出了一种基于先验知识引导的DETR框架，专门用于超声结节检测。与纯粹依赖数据驱动特征学习不同，该框架在多个网络阶段逐步引入不同的先验知识。 首先，在CNN骨干网中嵌入了具有先验正则化的空间自适应可变形前馈网络（SDFPR），以将几何先验注入到可变形采样中，从而稳定不规则和模糊结节的特征提取过程。其次，设计了一种多尺度空域频域特征混合器（MSFFM）来提取多尺度结构先验，在空间领域处理过程中突出轮廓连续性和边界线索，而在频率领域的建模则捕捉全局形态并抑制斑点噪声。 此外，密集特征交互机制（DFI）将这些受先验调制的特性在所有编码层中传播和利用，使解码器能够在一致的几何结构指导下提升查询精炼。我们在两个临床收集的甲状腺超声数据集（Thyroid I 和 Thyroid II）以及两个公开基准测试（TN3K 和 BUSI）上进行了实验，这些实验针对的是甲状腺和乳腺结节的检测问题。结果表明，所提出的方法在18种检测方法中表现出了卓越的准确性，特别是在形态复杂度高的情况下。 该方法的源代码可在以下链接处获取：[具体URL应由原文提供者给出]
URL
https://arxiv.org/abs/2601.02212
