MUGAT-KD:A Multimodal-to-Unimodal Knowledge Distillation Framework for Pulmonary Nodule Malignancy Classification

Pulmonary nodule malignancy classification is a vital component of early lung cancer screening. Multimodal models that integrate CT images with structured nodule attributes can derive more comprehensive nodule representations; however, the inference process relies on additional attribute annotations, limiting their practical applicability. Existing distillation methods primarily align model outputs or global representations, making it difficult for a unimodal model to preserve the hierarchical influence of multimodal knowledge on feature representations, attention to critical slices, and local regions of interest. To address this limitation, we propose MUGAT-KD, a multimodal-to-unimodal knowledge distillation framework. The framework employs a graph attention network to model the spatial relationships among CT slices and the associations between slices and nodule attributes. A frozen multimodal teacher then guides the student model at three levels: global features, slice-level attention, and saliency-aware patch masking. We conduct within-dataset classification experiments on LIDC-IDRIandLIDP datasetsandevaluate cross-dataset generalization by training on LIDC-IDRI and performing external validation on LIDP. Theexperimentalresults demonstrate that MUGAT-KD effectively transfers attribute knowledge from the multimodal teacher to the unimodal student, improving pulmonary nodule malignancy classification without requiring additional nodule attribute annotations and enhancing adaptability to differences in data standards. Ablation studies verify the complementary effects of the distillation modules. Attention visualizations further show that the student model learns the multimodal teacher’s attention patterns over critical CT slices and local regions. These results establish MUGAT-KD as an effective solution for pulmonary nodule malignancy classification when nodule attributes are unavailable.