Related Experiment Videos
A Dual-Branch Cross-Attention Fusion Network for Multi-Subtype Lung Cancer Classification
Libin Liu1, Yanfang Huang2, Xiqing Li1
1Department of Radiation Oncology, Fuzhou Pulmonary Hospital of Fujian Province, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Background:
Accurate lung cancer subtyping from CT images is essential for treatment planning. However, manual interpretation suffers from inter-observer variability across morphologically similar subtypes.
Methods:
To overcome this limitation, we propose a dual-branch cross-attention fusion network integrating ConvNeXt-Small and Swin Transformer-Small. Specifically, this architecture captures both local textures and global structural representations. A learnable cross-attention module then fuses these streams into a 512-dimensional unified descriptor. We train our network on a four-class dataset.
Results:
It achieves 98.46% accuracy and 98.45% F1-score on the test set. Ultimately, our method significantly outperforms state-of-the-art baselines.
Conclusions:
The proposed framework demonstrates massive clinical potential for non-invasive subtyping, offering a robust tool for personalised treatment planning while reducing diagnostic subjectivity.