Topological Feature Fusion for Dermoscopic Skin Cancer Detection

Skin cancer is a common and potentially fatal disease where early detection can save lives, especially for melanoma. Current deep learning systems classify skin lesions well, but they mainly rely on appearance cues and may miss deeper structural patterns in lesions. We present TopoCon MP, a method that extracts multiparameter topological signatures from dermoscopic images to capture multiscale lesion structure, and fuses these signatures with Vision Transformers using a supervised contrastive objective. Across three public datasets, TopoCon MP consistently improves performance over strong pretrained CNN and ViT baselines, including in cross dataset transfer. Ablations show that multiparameter topology and contrastive fusion each contribute to the gains. The resulting topological channels also provide an interpretable view of lesion organization, aligning with clinically meaningful structures. Overall, TopoCon MP demonstrates that multipersistence based topology can serve as a complementary modality for robust skin cancer detection.