Abstract
Graph Neural Networks (GNNs) have emerged as a powerful approach for graph-based machine learning tasks. Previous work applied GNNs to image-derived graph representations for various downstream tasks such as classification or anomaly detection. These transformations include segmenting images, extracting features from segments, mapping them to nodes, and connecting them. However, to the best of our knowledge, no study has rigorously compared the effectiveness of the numerous potential image-to-graph transformation approaches for GNN-based graph-level anomaly detection (GLAD). In this study, we systematically evaluate the efficacy of multiple segmentation schemes, edge construction strategies, and node feature sets based on color, texture, and shape descriptors to produce suitable image-derived graph representations to perform graph-level anomaly detection. We conduct extensive experiments on dermoscopic images using state-of-the-art GLAD models, examining performance and efficiency in purely unsupervised, weakly supervised, and fully supervised regimes. Our findings reveal, for example, that color descriptors contribute the best standalone performance, while incorporating shape and texture features consistently enhances detection efficacy. In particular, our best unsupervised configuration using OCGTL achieves a competitive AUC-ROC score of up to 0.805 without relying on pretrained backbones like comparable image-based approaches. With the inclusion of sparse labels, the performance increases substantially to 0.872 and with full supervision to 0.914 AUC-ROC.
Abstract (translated)
图神经网络（GNNs）已成为基于图的机器学习任务的一种强大方法。之前的研究将GNN应用于各种下游任务中的图像衍生图表示，例如分类或异常检测。这些转换包括分割图像、从片段中提取特征、将其映射为节点并进行连接等步骤。然而，据我们所知，没有研究严谨地比较了针对基于GNN的图级异常检测（GLAD）的各种潜在图像到图转换方法的有效性。 在这项研究中，我们系统地评估了多种分割方案、边构建策略以及基于颜色、纹理和形状描述符的节点特征集的效果，以生成适合进行图级异常检测的图像衍生图表示。我们在皮肤镜像数据上进行了广泛的实验，使用最先进的GLAD模型，在完全无监督、弱监督和全监督环境中考察性能和效率。 我们的研究发现例如：单靠颜色描述符可以获得最佳独立表现，而结合形状和纹理特征则会持续提升检测效果。特别是，我们使用的最优无监督配置OCGTL在不依赖预训练骨干网络的情况下达到了最高0.805的AUC-ROC得分，这与类似图像基方法相比具有竞争力。加入稀疏标签后，性能显著提高至0.872 AUC-ROC，在完全监督环境下进一步提升到0.914 AUC-ROC。
URL
https://arxiv.org/abs/2508.11826
