Frontiers in Surgery
A transformer-based multi-modal fusion framework for laryngeal lesion classification using contact endoscopy
Purpose This study introduces a novel multi-modal transformer-based framework that integrates clinical data, radiomic features, and deep learning representations for automated classification of laryngeal lesions from contact endoscopy images. Methods We retrospectively enrolled 300 patients with laryngeal lesions from three independent medical centers (Centers A, B, and C), acquiring 7,847 contact endoscopy images with narrow band imaging. Clinical variables (18 features), radiomic features extracted using PyRadiom …