发表机构
PONS Incorporated; Department of Radiology, Department of Medicine, University of British Columbia（PONS公司; 大学医学部和放射学部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究乳腺超声分类中图像编码器选择对图构建和分类性能的影响，通过评估五种编码器，用其嵌入构建图并分类，发现更高容量编码器可提升图同质性和分类性能，确立编码器选择为关键因素，图同质性为性能联系指标。
AI 中文摘要
乳腺超声广泛用于筛查，但由于斑点噪声、采集变异性以及标准超声成像中良性和恶性病例的微弱区分，自动分析仍然具有挑战性。图卷积网络（GCN）最近成为一种有前途的方法。然而，尚不清楚图像编码器的选择如何影响图构建和下游分类性能。在这项工作中，我们系统地评估了五种用于基于GCN的乳腺超声分类的图像编码器。使用图像嵌入构建余弦相似性k近邻图，并用带有线性分类头的单层GCN进行分类。在三个患者交叉验证折叠中，更高容量的编码器持续提高图同质性和下游分类性能。此外，测试集图同质性与分类准确率呈强线性相关。这些发现确立了编码器选择是基于图的乳腺超声分类的关键因素，并将图同质性确定为将表示质量与下游分类性能联系起来的关键指标。
英文摘要
Breast ultrasound is widely used for screening, yet automated analysis remains challenging due to speckle noise, acquisition variability, and weak separation of benign and malignant cases in standard ultrasound imaging. Graph convolutional networks (GCNs) have recently emerged as a promising approach by leveraging relationships among similar patient samples. However, it remains unclear how the choice of image encoder influences graph construction and downstream classification performance. In this work, we systematically evaluate five image encoders spanning convolutional and transformer-based architectures for GCN-based breast ultrasound classification. Image embeddings are used to construct cosine similarity k-nearest-neighbor graphs, which are classified using a single-layer GCN with a linear classification head. Across three patientwise cross-validation folds, higher-capacity encoders consistently improve graph homophily and downstream classification performance, yielding gains in accuracy, AUC, sensitivity, specificity, and F1-score. Moreover, test-set graph homophily exhibits a strong linear correlation with classification accuracy, with higher-capacity encoders consistently occupying the high-homophily, high-accuracy region suggesting that encoder-driven improvements in graph structure are a key mechanism underlying the observed performance gains. These findings establish encoder selection as a critical factor in graph-based breast ultrasound classification and identify graph homophily as a key indicator linking representation quality to downstream classification performance.
