Restricted Pyramid Graph Learning for Laryngopharyngeal Tumor Detection

Accurate detection of laryngo-pharyngeal tumors from endoscopic images is critical for early diagnosis, yet remains challenging because lesions are often small, low-contrast, and visually similar to surrounding mucosa or imaging artifacts. Classifying laryngo-pharyngeal endoscopic images requires both local lesion detail and surrounding anatomical context. Lesion-guided methods provide a region of interest, but their encoders usually process subsequent feature maps without an explicit spatial relation between coarse and fine evidence. As a result, background responses re-enter during fine-scale refinement, contaminating attention and obscuring subtle lesion boundaries. To bridge this gap, we present RPGNet, which combines mask-supervised dual-view learning with coarse-to-fine feature routing. An adapted SAM 2 generates lesion-focused views and provides spatial supervision for attention learning, while a shared GreedyViG encoder extracts complementary global and local representations without feeding lesion masks to the classifier. A Restricted-Pyramid Network adaptively allocates the retained feature budget according to regional complexity, preserving informative details while suppressing redundant background. On the internal FAHSYSU test set, RPGNet reaches 94.85% accuracy and 92.55% macro-F1. On three held-out external cohorts, its macro-F1 exceeds the strongest result in the comparison table by 3.41, 3.38, and 9.58 percentage points on SAHSYSU, NHSMU, and FAHSU, respectively. These findings support spatially constrained multiscale selection while motivating further work on minority-class recognition. Code is available at https://anonymous.4open.science/r/Q7M4V9K2D8R6X3TP/.