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。

基于位置嵌入的细粒度尿石诊断的配对距离学习

Location embedding based pairwise distance learning for fine-grained  diagnosis of urinary stones

Pre-training the embedding of a location generated from human mobility data
has become a popular method for location based services. In practice, modeling
the location embedding is too expensive, due to the large number of locations
to be trained in situations with fine-grained resolution or extensive target
regions. Previous studies have handled less than ten thousand distinct
locations, which is insufficient in the real-world applications. To tackle this
problem, we propose a Geo-Tokenizer, designed to efficiently reduce the number
of locations to be trained by representing a location as a combination of
several grids at different scales. In the Geo-Tokenizer, a grid at a larger
scale shares the common set of grids at smaller scales, which is a key factor
in reducing the size of the location vocabulary. The sequences of locations
preprocessed with the Geo-Tokenizer are utilized by a causal location embedding
model to capture the temporal dependencies of locations. This model dynamically
calculates the embedding vector of a target location, which varies depending on
its trajectory. In addition, to efficiently pre-train the location embedding
model, we propose the Hierarchical Auto-regressive Location Model objective to
effectively train decomposed locations in the Geo-Tokenizer. We conducted
experiments on two real-world user trajectory datasets using our pre-trained
location model. The experimental results show that our model significantly
improves the performance of downstream tasks with fewer model parameters
compared to existing location embedding methods.

通过预先训练基于位置的嵌入来提高位置服务的性能，并利用 Geo-Tokenizer 减少训练的位置数量，在考虑位置的时间依赖性的同时，有效地训练分解的位置，从而提供更少的模型参数并改善下游任务的性能。