The precise diagnosis of urinary stones is crucial for devising effective
treatment strategies. The diagnostic process, however, is often complicated by
the low contrast between stones and surrounding tissues, as well as the
variability in stone locations across different patients. To address this
issue, we propose a novel location embedding based pairwise distance learning
network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined
with location information for the fine-grained diagnosis of urinary stones.
LEPD-Net enhances the representation of stone-related features through
context-aware region enhancement, incorporates critical location knowledge via
stone location embedding, and achieves recognition of fine-grained objects with
our innovative fine-grained pairwise distance learning. Additionally, we have
established an in-house dataset on urinary tract stones to demonstrate the
effectiveness of our proposed approach. Comprehensive experiments conducted on
this dataset reveal that our framework significantly surpasses existing
state-of-the-art methods.

我们提出了一种基于位置嵌入的配对距离学习网络（LEPD-Net），通过低剂量腹部 X 射线成像结合位置信息，对尿道结石进行精细化诊断。我们的方法通过上下文感知区域增强增强了与结石相关的特征表示，通过结石位置嵌入融入了关键的位置知识，并且通过创新的精细化配对距离学习实现了对细粒度对象的识别。我们基于自建的尿道结石数据集进行了全面实验，结果显示我们的框架明显优于现有的先进方法。