DCAI: a dual cross-attention integration framework for benign-malignant classification of pulmonary nodules.

Lung cancer remains a leading cause of cancer-related mortality worldwide, and accurate early identification of malignant pulmonary nodules is critical for improving patient outcomes. Although artificial intelligence (AI) technology has shown promise in pulmonary nodule benign-malignant classification, existing methods struggle with modality heterogeneity and limited exploitation of complementary information across modalities. To address the above issues, we propose a novel multimodal framework, the Dual Cross-Attention Integration framework (DCAI), for benign-malignant classification of pulmonary nodules. Specifically, we first convert 3D nodules into multiple 2D images and obtain nodule features annotated by clinical experts. These features are encoded using Transformer models, and then a dual cross-attention module is proposed to dynamically align and interact with the complementary information between the different modalities. The fused representations from both modalities are then concatenated for benign-malignant prediction. We evaluate our proposed method on the LIDC-IDRI dataset, and experimental results demonstrate that DCAI outperforms several existing multimodal methods, highlighting the effectiveness of our approach in improving the accuracy of pulmonary nodule benign-malignant classification.