Vision Transformer在肾结石图像分类中优于传统CNN，尤其在复杂成像条件下。
Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs
- 用ViT替代CNN，通过自注意力捕捉长距离依赖关系。
- 复杂图像子集下准确率95.2%，远超CNN的64.5%。
- 适合医学影像分析、需要高精度分类的研究者使用。
从内窥镜图像中对肾结石进行分类对于个性化治疗和复发预防至关重要。尽管卷积神经网络（CNN）在此任务中表现良好，但其捕捉长程依赖的能力有限，在不同成像条件下可能影响性能。本研究对比了视觉变换器（ViT）与基于CNN的模型，评估它们在两个体外数据集上的表现，包含CCD相机和柔性输尿管镜图像。预训练于ImageNet-21k的ViT-base模型在多种成像条件下始终优于ResNet50基线。例如，在最复杂的图像子集（内窥镜图像的片段）中，ViT模型达到95.2%的准确率和95.1%的F1分数，而ResNet50仅为64.5%和59.3%。在混合视角的CCD相机图像子集中，ViT准确率达87.1%，高于CNN的78.4%。这些提升也体现在精确率和召回率上。结果表明，基于ViT的架构在肾结石图像分析中表现出更优的分类性能，为传统CNN提供了一种可扩展的替代方案。
原文摘要 · Abstract (English)
Kidney stone classification from endoscopic images is critical for personalized treatment and recurrence prevention. While convolutional neural networks (CNNs) have shown promise in this task, their limited ability to capture long-range dependencies can hinder performance under variable imaging conditions. This study presents a comparative analysis between Vision Transformers (ViTs) and CNN-based models, evaluating their performance on two ex vivo datasets comprising CCD camera and flexible ureteroscope images. The ViT-base model pretrained on ImageNet-21k consistently outperformed a ResNet50 baseline across multiple imaging conditions. For instance, in the most visually complex subset (Section patches from endoscopic images), the ViT model achieved 95.2% accuracy and 95.1% F1-score, compared to 64.5% and 59.3% with ResNet50. In the mixed-view subset from CCD-camera images, ViT reached 87.1% accuracy versus 78.4% with CNN. These improvements extend across precision and recall as well. The results demonstrate that ViT-based architectures provide superior classification performance and offer a scalable alternative to conventional CNNs for kidney stone image analysis.
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