R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
This work addresses the challenge of accurate cancer document classification for healthcare research, offering a resource-efficient alternative to transformers, though it is incremental as it adapts existing graph and attention techniques to a specific domain.
The paper tackled the problem of classifying cancer-related biomedical abstracts with limited labeled data and high computational demands by proposing a Residual Graph Attention Network (R-GAT), which achieved competitive performance comparable to transformer models like BioBERT and strong baselines while using fewer resources, as shown on a dataset of 1,875 PubMed abstracts.
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.