Gastrointestinal stromal tumour (GIST) lesions are mesenchymal neoplasms commonly found in the upper gastrointestinal tract, but non-invasive GIST detection during an endoscopy remains challenging because their ultrasonic images resemble several benign lesions. Techniques for automatic GIST detection and other lesions from endoscopic ultrasound (EUS) images offer great potential to advance the precision and automation of traditional endoscopy and treatment procedures. However, GIST recognition faces several intrinsic challenges, including the input restriction of a single image modality and the mismatch between tasks and models. To address these challenges, we propose a novel Query 2 (Query over Queries) framework to identify GISTs from ultrasound images. The proposed Query 2 framework applies an anatomical location embedding layer to break the single image modality. A cross-attention module is then applied to query the queries generated from the basic detection head. Moreover, a single-object restricted detection head is applied to infer the lesion categories. Meanwhile, to drive this network, we present GIST514-DB, a GIST dataset that will be made publicly available, which includes the ultrasound images, bounding boxes, categories and anatomical locations from 514 cases. Extensive experiments on the GIST514-DB demonstrate that the proposed Query 2 outperforms most of the state-of-the-art methods.