用结构化图模型提升3D肺部CT异常分类精度
Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans
- 将CT切片构建成结构图，通过谱卷积捕捉长程依赖
- 跨数据集泛化能力强，对纵向平移鲁棒
- 适合医学影像多标签分类任务，无需大规模预训练
随着CT检查数量增加，自动化方法如器官分割、异常检测和报告生成对缓解放射科医生工作负担至关重要。三维CT的多标签分类仍具挑战性，因体数据中存在复杂的空间关系且异常类型多样。现有基于3D卷积网络的方法难以建模长程依赖，而视觉变换器计算开销大，通常需同领域大规模预训练才能达到良好性能。本文提出一种新图基方法，将CT扫描表示为结构化图，利用轴向切片三元组作为节点，通过谱域卷积提升多标签异常分类表现。所提方法具备强跨数据集泛化能力，对z轴平移具有鲁棒性，且在消融实验中验证了各组件贡献。
原文摘要 · Abstract (English)
With the increasing number of CT scan examinations, there is a need for automated methods such as organ segmentation, anomaly detection and report generation to assist radiologists in managing their increasing workload. Multi-label classification of 3D CT scans remains a critical yet challenging task due to the complex spatial relationships within volumetric data and the variety of observed anomalies. Existing approaches based on 3D convolutional networks have limited abilities to model long-range dependencies while Vision Transformers suffer from high computational costs and often require extensive pre-training on large-scale datasets from the same domain to achieve competitive performance. In this work, we propose an alternative by introducing a new graph-based approach that models CT scans as structured graphs, leveraging axial slice triplets nodes processed through spectral domain convolution to enhance multi-label anomaly classification performance. Our method exhibits strong cross-dataset generalization, and competitive performance while achieving robustness to z-axis translation. An ablation study evaluates the contribution of each proposed component.
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