Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation.

中文翻译：

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通过 Vision Transformer 模型使用高分辨率计算机断层扫描图像改进尿石症检测。

尿路结石会引起侧腹痛，是年轻群体中的常见病症。诊断通常包括评估症状、进行体格检查、进行尿液检查和利用放射成像。人工智能模型在检测宝石方面表现出了卓越的能力。然而，由于数据集不足，这些模型的性能尚未达到适合实际应用的水平。因此，本研究引入了一种基于视觉变换器 (ViT) 的管道，用于使用增强的计算机断层扫描图像来检测尿路结石。