用少量样本实现肾结石类型精准识别，提升内窥镜诊断效率
Evaluation of Few-Shot Learning Methods for Kidney Stone Type Recognition in Ureteroscopy
- 采用原型网络的少样本学习方法，仅需少量图像即可训练
- 仅用25%数据量，性能媲美全量数据训练的传统模型
- 适合罕见结石类型或图像稀缺场景，临床实用性强
确定肾结石类型对防止复发至关重要。目前，通过参考体外鉴定方法获取结果需数周，而体内视觉识别则依赖高技能专家。为此，已开发深度学习模型以在输尿管镜检查中实现自动化分类。然而，这些模型普遍存在训练数据不足的问题。本文提出一种基于少样本学习的深度学习方法，旨在从极有限样本中提取足够区分性的特征，用于内窥镜图像中的肾结石类型识别。该方法特别适用于内窥镜图像稀缺或存在罕见类别的情况，支持在小规模训练集上完成分类。实验表明，原型网络（Prototypical Networks）在使用最多25%训练数据时，性能可达到甚至超过使用完整数据集训练的传统深度学习模型。
原文摘要 · Abstract (English)
Determining the type of kidney stones is crucial for prescribing appropriate treatments to prevent recurrence. Currently, various approaches exist to identify the type of kidney stones. However, obtaining results through the reference ex vivo identification procedure can take several weeks, while in vivo visual recognition requires highly trained specialists. For this reason, deep learning models have been developed to provide urologists with an automated classification of kidney stones during ureteroscopies. Nevertheless, a common issue with these models is the lack of training data. This contribution presents a deep learning method based on few-shot learning, aimed at producing sufficiently discriminative features for identifying kidney stone types in endoscopic images, even with a very limited number of samples. This approach was specifically designed for scenarios where endoscopic images are scarce or where uncommon classes are present, enabling classification even with a limited training dataset. The results demonstrate that Prototypical Networks, using up to 25% of the training data, can achieve performance equal to or better than traditional deep learning models trained with the complete dataset.
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