Lung Cancer Subtype Classification using Multi-Scale Fusion and Hierarchical Learning
Bildiriler > Tam Metin Bildiri
Yazarlar: Ziyang Wang, Hong Seng Gan, Jiayan Jiang, Selen BAYRAKTAROĞLU, Riries Rulaningtyas
2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), , , Uluslararası, Tam Metin Bildiri, ss 6117-6124
DOI: 10.1109/bibm66473.2025.11357229
Özet
Accurate classification of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of non-small cell lung cancer (NSCLC), remains challenging due to heterogeneous imaging characteristics and overlapping radiological-pathological patterns. To address this, we propose M2HCNet, a Multimodal and Multiscale Hierarchical Classification Network that integrates computed tomography (CT) and histopathology images for robust lung cancer subtype identification. A multi-scale feature extraction module captures both fine-grained cellular details and global contextual structures, enabling complementary fusion across modalities. The extracted features are then aligned using an optimal transport-based contrastive alignment strategy, which reduces distributional discrepancies and enhances cross-modal consistency under weakly paired conditions. A hierarchical classification mechanism further models inter-class relationships and coarse-to-fine label dependencies to improve discrimination between closely related subtypes. Experiments conducted on the TCGA-LUAD/LUSC cohort demonstrate that M2HCNet achieves 93.8% accuracy, 91.9% F1-score, and 95.5% AUC, outperforming ten recent state-of-the-art baselines. Ablation studies confirm the contributions of multi-scale fusion, cross-modal alignment, and hierarchical learning to overall performance. These findings suggest that M2HCNet effectively leverages heterogeneous yet complementary modalities for reliable lung cancer subtype classification. Future work will extend the framework to patient-matched and multi-institutional datasets to enhance generalization and clinical applicability.