用轻量图网络提升癌症文献分类，小数据下效果媲美大模型。
R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
- 基于图注意力与残差连接，捕捉医学文本语义关系。
- 在1875篇摘要上表现接近BioBERT，计算开销更低。
- 适合资源有限、需快速部署的医疗NLP场景。
准确分类癌症相关的生物医学摘要对推动癌症信息学和辅助医疗研究决策至关重要。然而，该领域常受限于标注语料稀缺及基于Transformer模型的高计算成本。为此，我们提出残差图注意力网络（R-GAT），融合多头注意力与残差连接，以捕获生物医学文本中的语义和关系依赖。在涵盖甲状腺、结肠、肺部及通用癌症主题的1875篇PubMed摘要数据集上，R-GAT表现稳定且具有竞争力，性能可与BioBERT、BioClinicalBERT等Transformer模型以及逻辑回归等经典基线相当，同时显著降低计算资源需求。消融实验证实注意力机制与残差连接在小样本条件下的重要性。为支持可复现性与未来研究，我们还公开了整理后的数据集。这些成果表明，轻量级图结构架构是生物医学NLP中高效替代复杂Transformer的可靠方案。
原文摘要 · Abstract (English)
Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often constrained by limited availability of labeled corpora and the high computational demands of transformer-based approaches. To address these challenges, we propose a Residual Graph Attention Network (R-GAT) that integrates multi-head attention with residual connections to capture semantic and relational dependencies in biomedical texts. Evaluated on a curated dataset of 1,875 PubMed abstracts spanning thyroid, colon, lung, and generic cancer topics, R-GAT achieves stable and competitive performance, comparable to transformer-based models such as BioBERT and BioClinicalBERT and strong classical baselines like Logistic Regression, while requiring significantly fewer computational resources. Ablation studies confirm the importance of attention and residual connections in ensuring robustness under limited-data conditions. To support reproducibility and facilitate future research, we also release the curated dataset. Together, these contributions demonstrate the value of lightweight graph-based architectures as reliable and resource-efficient alternatives to computationally intensive transformers in biomedical NLP.
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