Thyroid cancer is the most common endocrine malignancy, and its incidence is rising globally. While ultrasound is the preferred imaging modality for detecting thyroid nodules, its diagnostic accuracy is often limited by challenges such as low image contrast and blurred nodule boundaries. To address these issues, we propose Nodule-DETR, a novel detection transformer (DETR) architecture designed for robust thyroid nodule detection in ultrasound images. Nodule-DETR introduces three key innovations: a Multi-Spectral Frequency-domain Channel Attention (MSFCA) module that leverages frequency analysis to enhance features of low-contrast nodules; a Hierarchical Feature Fusion (HFF) module for efficient multi-scale integration; and Multi-Scale Deformable Attention (MSDA) to flexibly capture small and irregularly shaped nodules. We conducted extensive experiments on a clinical dataset of real-world thyroid ultrasound images. The results demonstrate that Nodule-DETR achieves state-of-the-art performance, outperforming the baseline model by a significant margin of 0.149 in mAP@0.5:0.95. The superior accuracy of Nodule-DETR highlights its significant potential for clinical application as an effective tool in computer-aided thyroid diagnosis. The code of work is available at https://github.com/wjj1wjj/Nodule-DETR.

翻译：甲状腺癌是最常见的内分泌恶性肿瘤，其发病率在全球范围内持续上升。虽然超声是检测甲状腺结节的首选成像方式，但其诊断准确性常因图像对比度低、结节边界模糊等挑战而受限。为解决这些问题，我们提出了Nodule-DETR，一种专为超声图像中鲁棒的甲状腺结节检测而设计的新型检测Transformer（DETR）架构。Nodule-DETR引入了三项关键创新：一个利用频域分析来增强低对比度结节特征的多频谱频域通道注意力（MSFCA）模块；一个用于高效多尺度融合的层次化特征融合（HFF）模块；以及一个灵活捕捉小型和不规则形状结节的多尺度可变形注意力（MSDA）模块。我们在一个真实世界的临床甲状腺超声图像数据集上进行了广泛的实验。结果表明，Nodule-DETR实现了最先进的性能，在mAP@0.5:0.95指标上显著优于基线模型0.149。Nodule-DETR的卓越准确性凸显了其作为计算机辅助甲状腺诊断有效工具的巨大临床应用潜力。本工作的代码发布于 https://github.com/wjj1wjj/Nodule-DETR。