Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification

2026-06-18

AI Summary

This work addresses the challenges posed by high structural heterogeneity, large intra-class variation, and subtle visual differences between benign and malignant lesions in dermoscopic images. To this end, the authors propose a superpixel-based multimodal fusion approach that models lesions as graphs whose nodes correspond to superpixels. Node features are extracted using a frozen CNN, while geometric relationships are incorporated as edge attributes. A novel metadata context node is introduced to enable native graph-level fusion of clinical information with visual features. Discriminative classification embeddings are generated through an edge-aware Graph Transformer coupled with an attention propagation mechanism. This method, which uniquely integrates superpixel graph structure, geometric edge attributes, and metadata context, achieves state-of-the-art performance across four public datasets, significantly improving both accuracy and robustness in benign–malignant skin lesion classification.

Abstract

Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual differences between benign and malignant cases. Existing CNN/ViT pipelines typically rely on global or patch-level features and often combine patient metadata via late fusion, which limits spatially grounded multimodal reasoning. We present a novel region-based graph learning framework that explicitly models lesions as graphs of spatially coherent superpixel regions represented as frozen CNN features. To capture fine-grained lesion arrangements, we encode inter-regional geometry as edge attributes and introduce a dedicated metadata context node connected to all regions, providing structured integration of demographic/clinical variables within the same relational space. Node representations are updated using our edge-aware graph transformer followed by attention-driven propagation, and a final graph-level embedding for benign-malignant classification. Experiments on four public benchmarks demonstrate that explicit region-level relational modeling and graph-native multimodal fusion yield consistent gains over the state-of-the-art. Consequently, we establish a new graph-centric perspective in which CNN features are modeled as relational nodes and improved through contextual integration, yielding more expressive and robust classifications.

Problem

• skin lesion classification
• dermoscopic images
• intra-class variability
• multimodal fusion
• spatially grounded reasoning

Innovation

• superpixel graph
• geometry-aware
• graph transformer
• metadata fusion
• skin lesion classification