TUS-DET: Open-vocabulary pretraining with standard planes for few-shot thyroid ultrasound lesion detection

Ultrasound imaging is widely used for thyroid disease assessment, yet few-shot thyroid lesion detection remains challenging because lesion appearance is often degraded by speckle noise, low contrast, ambiguous boundaries, and large acquisition-dependent variability. Moreover, thyroid interpretation relies heavily on standardized scanning planes and anatomical landmarks, whereas existing few-shot or open-vocabulary detectors usually learn lesion-level visual patterns without explicitly modeling such clinical context. In this work, we propose TUS-DET, a clinically grounded open-vocabulary detection framework for few-shot thyroid ultrasound lesion localization. The core idea is to transfer anatomical-semantic priors from thyroid ultrasound standard planes (TUSP) and key anatomical structures (KAS) to downstream lesion detection. Specifically, TUS-DET first learns region-text correspondences from standard-plane images and anatomical structures, and then adapts this prior to lesion localization with only a few annotated target samples. To achieve this, we introduce a thyroid ultrasound path-aggressive network (TUS-PAN) that injects text-derived anatomical semantics into multi-scale visual features, together with a learnable context mechanism that improves domain adaptation during few-shot fine-tuning. Experiments on multiple public thyroid ultrasound lesion datasets demonstrate that TUS-DET consistently improves few-shot detection performance over representative open-vocabulary detection baselines, particularly under extremely limited supervision. Additional analyses further show that TUSP-based pre-training provides effective anatomical grounding, improves downstream transferability, and maintains moderate computational cost compared with heavy grounding-style detectors. The code, bounding-box annotations, and pre-trained models will be released at https://github.com/PuppetDiary/TUS-DET.